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\chapter{Expected Value and Variance}\label{chp 6}

\section[Expected Value]{Expected Value of Discrete Random Variables}\label{sec 6.1}

When a large collection of numbers is assembled, as in a census, we are usually
interested not in the individual numbers, but rather in certain descriptive
quantities such as the average or the median.  In general, the same is true for the
probability distribution of a numerically-valued random variable.  In this and in the
next section, we shall discuss two such descriptive quantities: the \emx {expected
value} and the  \emx {variance.}  Both of these quantities apply only to
numerically-valued random variables, and so we assume, in these sections, that all
random variables have numerical values.  To give some intuitive justification for our
definition, we consider the following game.

\subsection*{Average Value}

A die is rolled.  If an odd number turns up, we win an amount equal to this number;
if an even number turns up, we lose an amount equal to this number.  For example, if
a two turns up we lose 2, and if a three comes up we win 3.  We want to decide if
this is a reasonable game to play.  We first try simulation.  The program {\bf Die}\index{Die
(program)} carries out this simulation.
\par
The program prints the frequency and the relative frequency with which each outcome
occurs.  It also calculates the average winnings.  We have run the program twice.  The results are
shown in Table~\ref{table 6.1}.

\medskip
\begin{table}[h]\centering
\centering
\begin{tabular}{|c|c|c|c|c|} \hline
        & \multicolumn{2}{c|}{n = 100}    & \multicolumn{2}{c|}{n = 10000} \\ \cline{2-5}
Winning & \hskip.1in Frequency \hskip.1in & Relative  & \hskip.1in Frequency \hskip.1in & Relative  \\
        &                                 & Frequency &                                 & Frequency
\\ \cline{1-5} 1       & 17        & .17                 & 1681       &.1681 \\  -2      & 17
& .17                 & 1678       &.1678 \\   3       & 16        & .16                 &
1626       &.1626 \\   -4      & 18        & .18                 & 1696       &.1696 \\
5       & 16        & .16                 & 1686       &.1686 \\
-6      & 16        & .16                 & 1633       &.1633 \\\hline
\end{tabular}
\caption{Frequencies for dice game.}
\label{table 6.1}
\end{table}

In the first run we have played the game 100 times.  In this run our average gain is
$-.57$.  It looks as if the game is unfavorable, and we wonder how unfavorable it
really is.  To get a better idea, we have played the game 10{,}000 times.  In this
case our average gain is $-.4949$.
\par
We note that the relative frequency of each of the six possible outcomes is quite
close to the probability 1/6 for this outcome.  This corresponds to our frequency
interpretation of probability.  It also suggests that for very large numbers of
plays, our average gain should be
\begin{eqnarray*}
\mu & = & 1 \Bigl(\frac 16\Bigr) - 2\Bigl(\frac 16\Bigr) + 3 \Bigl(\frac 16\Bigr) - 4
\Bigl(\frac 16\Bigr) + 5 \Bigl(\frac 16\Bigr) - 6 \Bigl(\frac 16\Bigr) \\
    & = & \frac 96 - \frac {12}6 = -\frac 36 = -.5\ .
\end{eqnarray*} This agrees quite well with our average gain for 10{,}000 plays.

We note that the value we have chosen for the average gain is obtained by taking the
possible outcomes, multiplying by the probability, and adding the results.  This
suggests the following definition for the expected outcome of an experiment.

\subsection*{Expected Value}

\begin{definition}\label{def 6.1}  Let $X$ be a numerically-valued discrete random
variable with sample space $\Omega$ and distribution function $m(x)$.  The {\em
expected value}\index{expected value} $E(X)$ is defined by
$$ E(X) = \sum_{x \in \Omega} x m(x)\ ,
$$ provided this sum converges absolutely.  We often refer to the expected value as
the \emx {mean,}\index{mean} and denote $E(X)$ by $\mu$ for short.  If the above sum does not
converge absolutely, then we say that $X$ does not have an expected value.
\end{definition}

\begin{example}\label{exam 6.03} Let an experiment consist of tossing a fair coin
three times.  Let $X$ denote the number of heads which appear.  Then the possible
values of $X$ are $0, 1, 2$ and $3$.  The corresponding probabilities are $1/8, 3/8,
3/8,$ and $1/8$.  Thus, the expected value of
$X$ equals
$$0\biggl(\frac 18\biggr) + 1\biggl(\frac 38\biggr) + 2\biggl(\frac 38\biggr) +
3\biggl(\frac 18\biggr) = \frac 32\ .$$   Later in this section we shall see a
quicker way to compute this expected value,  based on the fact that $X$ can be
written as a sum of simpler random variables.
\end{example}

\begin{example}\label{exam 6.05} Suppose that we toss a fair coin until a head first
comes up, and let $X$ represent the number of tosses which were made.  Then the
possible values of $X$ are $1, 2, \ldots$, and the distribution function of $X$ is
defined by
$$m(i) = {1\over {2^i}}\ .$$ (This is just the geometric distribution with parameter
$1/2$.)  Thus, we have
\begin{eqnarray*}  E(X) & = &\sum_{i = 1}^\infty i {1\over{2^i}} \\ & = & \sum_{i =
1}^\infty {1\over{2^i}} + \sum_{i = 2}^\infty {1\over{2^i}} + \cdots \\ & = & 1 +
{1\over 2} + {1\over{2^2}} + \cdots \\ & = & 2\ .
\end{eqnarray*}

\end{example}

\begin{example}(Example~\ref{exam 6.05} continued)\label{exam 6.055} Suppose
that we flip a coin until a head first appears, and if the number of tosses equals
$n$, then we are paid $2^n$ dollars.  What is the expected value of the payment?
\par We let $Y$ represent the payment.  Then,
$$P(Y = 2^n) = {1\over{2^n}}\ ,$$ for $n \ge 1$.  Thus,
$$E(Y) = \sum_{n = 1}^\infty 2^n {1\over{2^n}}\ ,$$ which is a divergent sum.  Thus,
$Y$ has no expectation.  This example is called the  \emx {St. Petersburg Paradox}.
\index{St. Petersburg Paradox}
The fact that the above sum is infinite suggests that a player should be willing to
pay any fixed amount per game for the privilege of playing this game. The reader is
asked to consider how much he or she would be willing to pay for this privilege.  It
is unlikely that the reader's answer is more than 10 dollars; therein lies the
paradox.
\par In the early history of probability, various mathematicians gave ways to resolve
this paradox.  One idea (due to G.~Cramer)\index{CRAMER, G.} consists of assuming that the amount
of money in the world is finite.  He thus assumes that there is some fixed value of $n$ such
that if the number of tosses equals or exceeds $n$, the payment is $2^n$ dollars.
The reader is asked to show in Exercise~\ref{exer 6.1.21} that the expected value of
the payment is now finite.
\par Daniel Bernoulli\index{BERNOULLI, D.} and Cramer also considered another way to
assign value to the payment.  Their idea was that the value of a payment is some function of the
payment; such a function is now called a utility function\index{utility function}.   Examples of
reasonable utility functions might include the square-root function or the logarithm function.  In
both cases, the value of $2n$ dollars is less than twice the value of $n$ dollars.  It can
easily be shown that in both cases, the expected utility of the payment is finite (see
Exercise~\ref{exer 6.1.21}).
\end{example}

\begin{example}\label{exam 6.8} Let $T$ be the time for the first success in a
Bernoulli trials process.  Then we take as sample space $\Omega$ the integers
$1,~2,~\ldots\ $ and assign the geometric distribution
$$ m(j) = P(T = j) = q^{j - 1}p\ . $$  Thus,
\begin{eqnarray*}  E(T) & = & 1 \cdot p + 2qp + 3q^2p +\cdots \\
     & = & p(1 + 2q + 3q^2 +\cdots )\ .
\end{eqnarray*}  Now if $|x| < 1$, then
$$ 1 + x + x^2 + x^3 + \cdots = \frac 1{1 - x}\ .$$  Differentiating this formula, we
get
$$ 1 + 2x + 3x^2 +\cdots = \frac 1{(1 - x)^2}\ ,$$  so
$$ E(T) = \frac p{(1 - q)^2} = \frac p{p^2} = \frac 1p\ .$$  In particular, we see
that if we toss a fair coin a sequence of times, the expected time until the first
heads is 1/(1/2) = 2.  If we roll a die a sequence of times, the expected number of
rolls until the first six is 1/(1/6) = 6.
\end{example}

\subsection*{Interpretation of Expected Value}

In statistics, one is frequently concerned with the average value of a set of data.
The following example shows that the ideas of average value and expected value are
very closely related.

\begin{example}\label{exam 6.8.5} The heights, in inches, of the women
on the Swarthmore basketball team are 5' 9", 5' 9", 5' 6", 5' 8", 5' 11",
5' 5", 5' 7", 5' 6", 5' 6", 5' 7", 5' 10", and 6' 0".
\par A statistician would compute the average height (in inches) as follows:
$$\frac{69 + 69 + 66 + 68 + 71 + 65 + 67 + 66 + 66 + 67 + 70 + 72}{12} = 67.9\ .$$
One can also interpret this number as the expected value
of a random variable.  To see this, let an experiment consist of choosing one of the
women at random, and let $X$ denote her height.  Then the expected
value of $X$ equals 67.9.
\end{example}

\par Of course, just as with the frequency interpretation of probability, to
interpret expected value as an average outcome requires further justification.  We
know that for any finite experiment the average of the outcomes is not predictable.
However, we shall eventually prove that the average will usually be close to $E(X)$
if we repeat the experiment a large number of times.  We first need to develop some
properties of the expected value.  Using these properties, and those of the concept
of the variance to be introduced in the next section, we shall be able to prove the
\emx {Law of Large Numbers.}  This theorem will justify mathematically both our
frequency concept of probability and the interpretation of expected value as the
average value to be expected in a large number of experiments.

\subsection*{Expectation of a Function of a Random Variable}

Suppose that $X$ is a discrete random variable with sample space $\Omega$, and
$\phi(x)$ is a real-valued function with domain $\Omega$.  Then $\phi(X)$ is a
real-valued random variable.  One way to determine the expected value of $\phi(X)$ is
to first determine the distribution function of this random variable, and then use
the definition of expectation. However, there is a better way to compute the expected
value of $\phi(X)$, as demonstrated in the next example.

\begin{example}\label{exam 6.1.5} Suppose a coin is tossed 9 times, with the result
\[ HHHTTTTHT\ . \]
\noindent The first set of three heads is called a  \emx {run}\index{run}.  There are three
more runs in this sequence, namely the next four tails, the next head, and the next
tail.  We do not consider the first two tosses to constitute a run, since the third
toss has the same value as the first two.
\par Now suppose an experiment consists of tossing a fair coin three times.  Find the
expected number of runs.   It will be helpful to think of two random variables, $X$
and $Y$, associated with this experiment.  We let $X$ denote the sequence of heads
and tails that results when the experiment is performed, and $Y$ denote the number of
runs in the outcome
$X$.  The possible outcomes of $X$ and the corresponding values of $Y$  are
shown in Table~\ref{table 6.2}.
\begin{table}
\centering
$$\begin{tabular}{cc} X    &  Y \\ \hline HHH  & 1\\ HHT  & 2\\ HTH  & 3\\ HTT  & 2\\
THH  & 2\\ THT  & 3\\ TTH  & 2\\ TTT  & 1\\
\end{tabular}
$$
\caption{Tossing a coin three times.}
\label{table 6.2}
\end{table}

To calculate $E(Y)$ using the definition of expectation, we first must find the
distribution function $m(y)$ of $Y$ i.e., we group together those values of $X$ with
a common value of
$Y$ and add their probabilities.  In this case, we calculate that the distribution
function of $Y$ is:  $m(1) = 1/4,\ m(2) = 1/2,$ and $m(3) = 1/4$.  One easily finds
that $E(Y) = 2$.
\par Now suppose we didn't group the values of $X$ with a common $Y$-value, but
instead, for each
$X$-value $x$, we multiply the probability of $x$ and the corresponding value of $Y$,
and add the results.  We obtain
$$1\biggl(\frac 18\biggr) +2\biggl(\frac 18\biggr) +3\biggl(\frac 18\biggr)
+2\biggl(\frac 18\biggr) +2\biggl(\frac 18\biggr) +3\biggl(\frac 18\biggr)
+2\biggl(\frac 18\biggr) +1\biggl(\frac 18\biggr)\ ,$$ which equals 2.
\par This illustrates the following general principle.  If $X$ and $Y$ are two random
variables, and $Y$ can be written as a function of $X$, then one can compute the
expected value of $Y$ using the distribution function of $X$.
\end{example}

\begin{theorem}\label{thm 6.3.5} If $X$ is a discrete random variable with sample
space
$\Omega$ and distribution function $m(x)$, and if
%$\phi : \Omega \to {\bf\rm R}$ is a function, then
$\phi : \Omega \to$ \mat{\rm R} is a function, then
$$ E(\phi(X)) = \sum_{x \in \Omega} \phi(x) m(x)\ ,
$$  provided the series converges absolutely.
\end{theorem}

The proof of this theorem is straightforward, involving nothing more than grouping
values of
$X$ with a common $Y$-value, as in Example~\ref{exam 6.1.5}.

\subsection*{The Sum of Two Random Variables}

Many important results in probability theory concern sums of random variables.  We
first  consider what it means to add two random variables.

\begin{example}\label{exam 6.06} We flip a coin and let $X$ have the value 1 if the
coin comes up heads and 0 if the coin comes up tails.  Then, we roll a die and let
$Y$ denote the face that comes up.  What does $X+Y$ mean, and what is its
distribution?  This question is easily answered in this case, by considering, as we
did in Chapter~\ref{chp 4}, the joint random variable $Z = (X,Y)$, whose outcomes are
ordered pairs of the form $(x, y)$, where $0 \le x \le 1$ and $1 \le y \le 6$.  The
description of the experiment makes it reasonable to assume that $X$ and $Y$ are
independent, so the distribution function of $Z$ is uniform, with $1/12$ assigned to
each outcome.  Now it is an easy matter to find the set of outcomes of $X+Y$, and its
distribution function.
\end{example}

In Example~\ref{exam 6.03}, the random variable $X$ denoted the number of heads which
occur when a fair coin is tossed three times.  It is natural to think of $X$ as the
sum of the random variables $X_1, X_2, X_3$, where $X_i$ is defined to be 1 if the
$i$th toss comes up heads, and 0 if the $i$th toss comes up tails.  The expected
values of the $X_i$'s are extremely easy to compute.  It turns out that the expected
value of $X$ can be obtained by simply adding the expected values of the $X_i$'s.
This fact is stated in the following theorem.

\begin{theorem}\label{thm 6.1} Let $X$ and $Y$ be random variables with finite
expected values.  Then
$$ E(X + Y) = E(X) + E(Y)\ ,
$$ and if $c$ is any constant, then
$$ E(cX) = cE(X)\ .
$$
\proof Let the sample spaces of $X$ and $Y$ be denoted by $\Omega_X$ and $\Omega_Y$,
and suppose that
$$\Omega_X = \{x_1, x_2, \ldots\}$$ and
$$\Omega_Y = \{y_1, y_2, \ldots\}\ .$$ Then we can consider the random variable $X +
Y$ to be the result of applying the  function $\phi(x, y) = x + y$ to the joint
random variable $(X,Y)$.  Then, by Theorem~\ref{thm 6.3.5}, we have
\begin{eqnarray*} E(X+Y) & = &\sum_j \sum_k (x_j + y_k) P(X = x_j,\ Y = y_k) \\
       & = &\sum_j \sum_k x_j P(X = x_j,\ Y = y_k) +
\sum_j \sum_k y_k P(X = x_j,\ Y = y_k) \\ & = &\sum_j x_j P(X = x_j) + \sum_k y_k P(Y
= y_k)\ .
\end{eqnarray*} The last equality follows from the fact that
$$\sum_k P(X = x_j,\ Y = y_k)\ \  =\ \  P(X = x_j)$$ and
$$\sum_j P(X = x_j,\ Y = y_k)\ \  =\ \  P(Y = y_k)\ .$$ Thus,
$$E(X+Y) = E(X) + E(Y)\ .$$ If $c$ is any constant,
\begin{eqnarray*}  E(cX) & = & \sum_j cx_j P(X = x_j) \\
      & = & c\sum_j x_j P(X = x_j)\\
      & = & cE(X)\ .
\end{eqnarray*}
\end{theorem}
It is easy to prove by mathematical induction that  \emx {the expected value
of the sum of any finite number of random variables is the sum of the expected values
of the individual random variables.}
\par It is important to note that mutual independence of the summands was not needed
as a hypothesis in the Theorem~\ref{thm 6.1} and its generalization.  The fact that
expectations add, whether or not the summands are mutually independent, is sometimes
referred to as the First Fundamental Mystery of Probability.\index{First Fundamental Mystery of
Probability}

\begin{example}\label{exam 6.1} Let $Y$ be the number of fixed points in a random
permutation of the set
$\{a,b,c\}$.  To find the expected value of $Y$, it is helpful to consider the basic
random variable associated with this experiment, namely the random variable $X$ which
represents the random permutation.  There are six possible outcomes of $X$, and we
assign to each of them the probability $1/6$ see Table~\ref{table 6.3}.  Then we can calculate $E(Y)$ using
Theorem~\ref{thm 6.3.5}, as
$$3\Bigl({1\over 6}\Bigr) + 1\Bigl({1\over 6}\Bigr) + 1\Bigl({1\over 6}\Bigr) +
0\Bigl({1\over 6}\Bigr) +  0\Bigl({1\over 6}\Bigr) + 1\Bigl({1\over 6}\Bigr) = 1\ .
$$
\begin{table}
\centering
\begin{tabular}{ccc} &                          & \\
\hline &$X$                 & $Y$ \\
\hline  &$a\;\;\;b\;\;\;   c$       & 3 \\  &$a\;\;\; c\;\;\;  b$       & 1 \\
&$b\;\;\; a\;\;\;  c$       & 1 \\  &$b\;\;\; c\;\;\;  a$       & 0 \\  &$c\;\;\;
a\;\;\;  b$       & 0 \\  &$c\;\;\; b\;\;\;  a$       & 1 \\
\hline
\end{tabular}
\caption{Number of fixed points.}
\label{table 6.3}
\end{table}

\par  We now give a very quick way to calculate the average number of fixed points in
a random permutation of the set $\{1, 2, 3, \ldots, n\}$.  Let $Z$ denote the random
permutation.  For each $i$, $1 \le i \le n$, let $X_i$ equal 1 if $Z$ fixes $i$, and
0 otherwise.  So if we let $F$ denote the number of fixed points in $Z$, then
$$F = X_1 + X_2 + \cdots + X_n\ .$$ Therefore, Theorem~\ref{thm 6.1} implies that
$$E(F) = E(X_1) + E(X_2) + \cdots + E(X_n)\ .$$ But it is easy to see that for each
$i$,
$$E(X_i) = {1\over n}\ ,$$ so
$$E(F) = 1\ .$$ This method of calculation of the expected value is frequently very
useful.  It applies whenever the random variable in question can be written as a sum
of simpler random variables.  We emphasize again that it is not necessary that the
summands be mutually independent.
\end{example}

\subsection*{Bernoulli Trials}

\begin{theorem}\label{thm 6.3} Let $S_n$ be the number of successes in $n$ Bernoulli
trials with probability
$p$ for success on each trial.  Then the expected number of successes is $np$.  That
is,
$$ E(S_n) = np\ .
$$
\proof Let $X_j$ be a random variable which has the value~1 if the $j$th outcome is a
success and~0 if it is a failure.  Then, for each $X_j$,
$$ E(X_j) = 0\cdot(1 - p) + 1\cdot p = p\ .
$$ Since
$$ S_n = X_1 + X_2 +\cdots+ X_n\ ,
$$ and the expected value of the sum is the sum of the expected values, we have
\begin{eqnarray*} E(S_n) & = & E(X_1) + E(X_2) +\cdots+ E(X_n) \\
       & = & np\ .
\end{eqnarray*}
\end{theorem}

\subsection*{Poisson Distribution}

Recall that the Poisson distribution with parameter $\lambda$ was obtained as a limit
of binomial distributions with parameters $n$ and $p$, where it was assumed that $np =
\lambda$, and $n \rightarrow \infty$.  Since for each $n$, the corresponding binomial
distribution has expected value $\lambda$, it is reasonable to guess that the expected
value of a Poisson distribution with parameter $\lambda$ also has expectation equal to
$\lambda$. This is in fact the case, and the reader is invited to show this (see
Exercise~\ref{exer 6.1.100}).

\subsection*{Independence}

If $X$ and $Y$ are two random variables, it is not true in general that $E(X
\cdot Y) = E(X)E(Y)$.  However, this is true if $X$ and $Y$ are  \emx {independent.}

\begin{theorem}\label{thm 6.4} If $X$ and $Y$ are independent random variables, then
$$ E(X \cdot Y) = E(X)E(Y)\ .
$$
\proof Suppose that
$$\Omega_X = \{x_1, x_2, \ldots\}$$  and
$$\Omega_Y = \{y_1, y_2, \ldots\}$$ are the sample spaces of $X$ and $Y$,
respectively.  Using Theorem~\ref{thm 6.3.5}, we have
$$ E(X \cdot Y) = \sum_j \sum_k x_jy_k P(X = x_j,\ Y = y_k)\ .
$$

But if $X$ and $Y$ are independent,
$$ P(X = x_j, Y = y_k) = P(X = x_j)P(Y = y_k)\ .
$$ Thus,
\begin{eqnarray*} E(X \cdot Y) & = & \sum_j\sum_k x_j y_k P(X = x_j) P(Y = y_k) \\
             & = & \left(\sum_j x_j P(X = x_j)\right) \left(\sum_k y_k P(Y =
y_k)\right) \\
             & = &E(X) E(Y)\ .
\end{eqnarray*}
\end{theorem}

\begin{example}\label{exam 6.3} A coin is tossed twice.  $X_i = 1$ if the $i$th toss
is heads and~0 otherwise.  We know that $X_1$ and $X_2$ are independent.  They each
have expected value 1/2.  Thus $E(X_1 \cdot X_2) = E(X_1) E(X_2) = (1/2)(1/2) = 1/4$.
\end{example}

We next give a simple example to show that the expected values need not multiply if
the random variables are not independent.

\begin{example}\label{exam 6.4} Consider a single toss of a coin.  We define the
random variable $X$ to be~1 if heads turns up and~0 if tails turns up, and we set $Y
= 1 - X$.  Then $E(X) = E(Y) = 1/2$.  But $X \cdot Y = 0$ for either outcome.  Hence,
$E(X \cdot Y) = 0 \ne E(X) E(Y)$.
\end{example}

We return to our records example of Section~\ref{sec 3.1} for another application of
the result that the expected value of the sum of random variables is the sum of the
expected values of the individual random variables.

\subsection*{Records}\index{records}

\begin{example}\label{exam 6.5} We start keeping snowfall records this year and want
to find the expected number of records that will occur in the next $n$ years.  The
first year is necessarily a record.  The second year will be a record if the snowfall
in the second year is greater than that in the first year.  By symmetry, this
probability is 1/2.  More generally, let $X_j$ be~1 if the $j$th year is a record
and~0 otherwise.  To find $E(X_j)$, we need only find the probability that the $j$th
year is a record.  But the record snowfall for the first $j$ years is equally likely
to fall in any one of these years, so $E(X_j) = 1/j$.  Therefore, if $S_n$ is the
total number of records observed in the first $n$ years,
$$ E(S_n) = 1 + \frac 12 + \frac 13 +\cdots+ \frac 1n\ .
$$ This is the famous  \emx {divergent harmonic series.}  It is easy to show that
$$ E(S_n) \sim \log n
$$ as $n \rightarrow \infty$.  A more accurate approximation to $E(S_n)$ is given by the expression
$$\log n + \gamma + {1\over {2n}}\ ,$$
where $\gamma$ denotes Euler's constant, and is approximately equal to .5772.

Therefore, in ten years the expected number of records is approximately $2.9298$; the exact value is the sum of the first ten terms of the harmonic series which
is 2.9290.
\end{example}

\subsection*{Craps}\index{craps}

\begin{example}\label{exam 6.6} In the game of craps, the player makes a bet and
rolls a pair of dice.  If the sum of the numbers is 7~or~11 the player wins, if it is
2,~3, or~12 the player loses.  If any other number results, say $r$, then $r$ becomes
the player's point and he continues to roll until either $r$ or 7 occurs.  If $r$
comes up first he wins, and if 7 comes up first he loses.  The program {\bf Craps}\index{Craps
(program)} simulates playing this game a number of times.
\par We have run the program for 1000 plays in which the player bets 1~dollar each
time.  The player's average winnings were $-.006$.  The game of craps would seem to
be only slightly unfavorable.  Let us calculate the expected winnings on a single
play and see if this is the case.  We construct a two-stage tree measure as shown in
Figure~\ref{fig 6.1}.

\putfig{4.5truein}{PSfig6-1}{Tree measure for craps.}{fig 6.1}

The first stage represents the possible sums for his first roll.  The second stage
represents the possible outcomes for the game if it has not ended on the first roll.
In this stage we are representing the possible outcomes of a sequence of rolls
required to determine the final outcome.  The branch probabilities for the first
stage are computed in the usual way assuming all 36 possibilites for outcomes for the
pair of dice are equally likely.  For the second stage we assume that the game will
eventually end, and we compute the conditional probabilities for obtaining either the
point or a~7.  For example, assume that the player's point is~6.  Then the game will
end when one of the eleven pairs, $(1,5)$, $(2,4)$, $(3,3)$, $(4,2)$, $(5,1)$,
$(1,6)$, $(2,5)$,
$(3,4)$, $(4,3)$, $(5,2)$, $(6,1)$, occurs.  We assume that each of these possible
pairs has the same probability.  Then the player wins in the first five cases and
loses in the last six.  Thus the probability of winning is 5/11 and the probability
of losing is 6/11.  From the path probabilities, we can find the probability that the
player wins 1~dollar; it is 244/495.  The probability of losing is then 251/495.
Thus if $X$ is his winning for a dollar bet,
\begin{eqnarray*} E(X) & = & 1\Bigl(\frac {244}{495}\Bigr) + (-1)\Bigl(\frac
{251}{495}\Bigr) \\
     & = & -\frac {7}{495} \approx -.0141\ .
\end{eqnarray*} The game is unfavorable, but only slightly.  The player's expected
gain in $n$ plays is $-n(.0141)$.  If $n$ is not large, this is a small expected loss
for the player.  The casino makes a large number of plays and so can afford a small
average gain per play and still expect a large profit.
\end{example}

\subsection*{Roulette}\index{roulette}

\begin{example}\label{exam 6.7} In Las Vegas, a roulette wheel has 38 slots numbered
0,\ 00,\ 1,\ 2,\ \ldots,\ 36.  The 0 and 00 slots are green, and half of the remaining
36 slots are red and half are black.  A croupier spins the wheel and throws an ivory ball.
If you bet 1 dollar on red, you win 1 dollar if the ball stops in a red slot, and otherwise
you lose a dollar.  We wish to calculate the expected value of your winnings, if you bet 1
dollar on red.
\par
Let $X$ be the random variable which denotes your winnings in a 1 dollar bet on red in Las Vegas
roulette. Then the distribution of $X$ is given by
$$ m_{X} = \pmatrix{
   -1 & 1 \cr 20/38 & 18/38 \cr},
$$ and one can easily calculate (see Exercise~\ref{exer 6.1.5}) that
$$
E(X) \approx -.0526\ .
$$
\par
We now consider the roulette game in Monte Carlo, and follow the treatment of
Sagan.\footnote{H. Sagan, \emx {Markov Chains in Monte Carlo,} Math. Mag., vol. 54, no. 1 (1981),
pp. 3-10.}\index{SAGAN, H.} In the roulette game in Monte Carlo there is only one~0.  If you
bet 1 franc on red and a~0 turns up,  then, depending upon the casino, one or more of the following
options may be offered:
\par
\noindent
(a) You get 1/2 of your bet back, and the casino gets the other half of your bet.
\par
\noindent
(b) Your bet is put ``in prison," which we will denote by $P_1$.  If red comes up on the next turn,
you get your bet back (but you don't win any money).  If black or 0 comes up, you lose your bet.
\par
\noindent
(c) Your bet is put in prison $P_1$, as before.  If red comes up on the next turn, you get
your bet back, and if black comes up on the next turn, then you lose your bet.  If a 0
comes up on the next turn, then your bet is put into double prison, which we will denote by
$P_2$.  If your bet is in double prison, and if red comes up on the next turn, then your bet is
moved back to prison $P_1$ and the game proceeds as before.  If your bet is in double prison, and
if black or 0 come up on the next turn, then you lose your bet.  We refer the reader to
Figure~\ref{fig 6.1.5}, where a tree for this option is shown.  In this figure, $S$ is the
starting position, $W$ means that you win your bet, $L$ means that you lose your bet, and $E$ means
that you break even.
\putfig{4.5truein}{PSfig6-1-5}{Tree for 2-prison Monte Carlo roulette.}{fig 6.1.5}
\par
It is interesting to compare the expected winnings of a 1 franc bet on red, under each
of these three options.  We leave the first two calculations as an exercise
(see Exercise~\ref{exer 6.1.38}).
Suppose that you choose to play alternative (c).
The calculation for this case illustrates the way that the early French probabilists worked problems
like this.
\par
Suppose you bet on red, you choose alternative (c), and a~0 comes up.  Your
possible future outcomes are shown in the tree diagram in Figure~\ref{fig 6.2}.
Assume that your money is in the first prison and let $x$ be the probability that you
lose your franc.  From the tree diagram we see that
$$
x = \frac {18}{37} + \frac 1{37}P({\rm you\ lose\ your\ franc\ }|\ {\rm your\ franc\ is
\ in\ }P_2)\ .
$$
Also,
$$
P({\rm you\ lose\ your\ franc\ }|\ {\rm your\ franc\ is
\ in\ }P_2) =  \frac {19}{37} + \frac{18}{37}x\ .
$$
So, we have
$$x = \frac{18}{37} + \frac 1{37}\Bigl(\frac {19}{37} + \frac{18}{37}x\Bigr)\ .$$
Solving for $x$, we obtain $x = 685/1351$.  Thus, starting at $S$, the probability that you lose
your bet equals
$$\frac {18}{37} + \frac 1{37}x = \frac{25003}{49987}\ .$$
\par
To find the probability that you win when you bet on red, note that you can only win if
red comes up on the first turn, and this happens with probability 18/37.
Thus your expected winnings are
$$ 1 \cdot {\frac{18}{37}} -1 \cdot {\frac {25003}{49987}} = -\frac{687}{49987}
\approx -.0137\ .$$

\putfig{4.5truein}{PSfig6-2}{Your money is put in prison.}  {fig 6.2}

It is interesting to note that the more romantic option (c) is less favorable than option (a)
(see Exercise~\ref{exer 6.1.38}).
\par
If you bet 1~dollar on the number 17, then the distribution function for your
winnings $X$ is
$$ P_X = \pmatrix{
   -1 & 35 \cr 36/37 & 1/37 \cr}\ ,
$$
and the expected winnings are
$$ -1 \cdot {\frac{36}{37}} + 35 \cdot {\frac 1{37}} = -\frac 1{37} \approx -.027\ .
$$
Thus, at Monte Carlo different bets have different expected values.  In Las Vegas
almost all bets have the same expected value of $-2/38 = -.0526$ (see Exercises~\ref{exer 6.1.4} and
\ref{exer 6.1.5}).
\end{example}

\subsection*{Conditional Expectation}

\begin{definition}\label{def 6.3} If $F$ is any event and $X$ is a random variable
with sample space $\Omega = \{x_1, x_2,
\ldots\}$, then the  \emx {conditional expectation\index{conditional expectation} given $F$} is
defined by
$$ E(X|F) = \sum_j x_j P(X = x_j|F)\ .
$$ Conditional expectation is used most often in the form provided by the following
theorem.
\end{definition}

\begin{theorem}\label{thm 6.5} Let $X$ be a random variable with sample space
$\Omega$.  If $F_1$,~$F_2$, \dots,~$F_r$ are events such that $F_i \cap F_j =
\emptyset$ for $i \ne j$ and $\Omega = \cup_j F_j$, then
$$ E(X) = \sum_j E(X|F_j) P(F_j)\ .
$$
\proof We have
\begin{eqnarray*}
\sum_j E(X|F_j) P(F_j) & = & \sum_j \sum_k x_k P(X = x_k|F_j) P(F_j) \\
                       & = & \sum_j \sum_k x_k P(X = x_k\,\, {\rm and}\,\,
F_j\,\,{\rm occurs}) \\
                       & = & \sum_k \sum_j x_k P(X = x_k\,\,{\rm and}\,\,F_j\,\,{\rm
occurs})
\\
                       & = & \sum_k x_k P(X = x_k) \\
                       & = & E(X)\ .
\end{eqnarray*}
\end{theorem}

\begin{example}(Example~\ref{exam 6.6} continued){\label{exam 6.10}}\index{craps} Let $T$ be
the number of rolls in a single play of craps.  We can think of a single play as a two-stage
process.  The first stage consists of a single roll of a pair of dice.  The play is over if this
roll is a 2, 3, 7, 11, or 12.  Otherwise, the player's point is established, and the second stage
begins.  This second stage consists of a sequence of rolls which ends when either the player's point
or a 7 is rolled.  We record the outcomes of this two-stage experiment using the random variables
$X$ and $S$, where $X$ denotes the first roll, and $S$ denotes the number of rolls in the second
stage of the experiment (of course, $S$ is sometimes equal to 0).  Note that $T = S+1$.
Then by Theorem~\ref{thm 6.5}
$$ E(T) = \sum_{j = 2}^{12} E(T|X = j) P(X = j)\ .
$$ If $j = 7$,~11 or~2, 3,~12, then $E(T|X = j) = 1$.   If $j = 4, 5, 6, 8, 9,$ or
$10$, we can use Example~\ref{exam 6.8} to calculate the expected value of $S$.  In each of
these cases, we continue rolling until we get either a
$j$ or a 7.  Thus, $S$ is
geometrically distributed with parameter $p$, which depends upon $j$.  If
$j = 4$, for example, the value of $p$ is $3/36 + 6/36 = 1/4$.  Thus, in this case,
the expected number of additional rolls is $1/p = 4$, so
$E(T|X = 4) = 1 + 4 = 5$.  Carrying out the corresponding calculations for the other
possible values of $j$ and using Theorem~\ref{thm 6.5} gives
\begin{eqnarray*} E(T) & = & 1\Bigl(\frac {12}{36}\Bigr)
    + \Bigl(1 + \frac {36}{3 + 6}\Bigr)\Bigl(\frac 3{36}\Bigr)
    + \Bigl(1 + \frac {36}{4 + 6}\Bigr)\Bigl(\frac 4{36}\Bigr) \\
& & + \Bigl(1 + \frac {36}{5 + 6}\Bigr)\Bigl(\frac 5{36}\Bigr)
    + \Bigl(1 + \frac {36}{5 + 6}\Bigr)\Bigl(\frac 5{36}\Bigr) \\
& & + \Bigl(1 + \frac {36}{4 + 6}\Bigr)\Bigl(\frac 4{36}\Bigr)
    + \Bigl(1 + \frac {36}{3 + 6}\Bigr)\Bigl(\frac 3{36}\Bigr) \\
& = & \frac {557}{165} \\
& \approx & 3.375\dots\ .
\end{eqnarray*}
\end{example}

\subsection*{Martingales}

We can extend the notion of fairness to a player playing a sequence of games by using
the concept of conditional expectation.

\begin{example}\label{exam 6.11} Let $S_1$,~$S_2$, \dots,~$S_n$ be Peter's
accumulated fortune in playing heads or tails (see Example~\ref{exam 1.3}).  Then
$$ E(S_n | S_{n - 1} = a,\dots,S_1 = r) = \frac 12 (a + 1) + \frac 12 (a - 1) = a\ .
$$

We note that Peter's expected fortune after the next play is equal to his present
fortune.  When this occurs, we say the game is \emx {fair.}\index{fair game}  A fair game is also
called a \emx {martingale.}\index{martingale}  If the coin is biased and comes up heads with
probability
$p$ and tails with probability $q = 1 - p$, then
$$ E(S_n | S_{n - 1} = a,\dots,S_1 = r) = p (a + 1) + q (a - 1) = a + p - q\ .
$$ Thus, if $p < q$, this game is unfavorable, and if $p > q$, it is favorable.
\end{example}

If you are in a casino, you will see players adopting elaborate \emx {systems}\index{gambling
systems} of play to try to make unfavorable games favorable.  Two such systems, the martingale
doubling system and the more conservative Labouchere system, were described in
Exercises~\ref{sec 1.1}.\ref{exer 1.1.9}~and~\ref{sec 1.1}.\ref{exer 1.1.10}.
Unfortunately, such systems cannot change even a fair game into a favorable game.

Even so, it is a favorite pastime of many people to develop systems of play for
gambling games and for other games such as the stock market.  We close this section
with a simple illustration of such a system.

\subsection*{Stock Prices}\index{stock prices}

\begin{example}\label{exam 6.12} Let us assume that a stock increases or decreases in
value each day by 1~dollar, each with probability 1/2.  Then we can identify this
simplified model with our familiar game of heads or tails.  We assume that a buyer,
Mr.\ Ace\index{Ace, Mr.}, adopts the following strategy.  He buys the stock on the first day at
its price $V$.  He then waits until the price of the stock increases by one to
$V + 1$ and sells.  He then continues to watch the stock until its price falls back
to $V$.  He buys again and waits until it goes up to $V + 1$ and sells.  Thus he
holds the stock in intervals during which it increases by 1~dollar.  In each such
interval, he makes a profit of 1~dollar.  However, we assume that he can do this only
for a finite number of trading days.  Thus he can lose if, in the last interval that
he holds the stock, it does not get back up to $V + 1$; and this is the only way he
can lose.  In Figure~\ref{fig 6.3} we illustrate a typical history if Mr.\ Ace must
stop in twenty days.
\putfig{4truein}{PSfig6-3}{Mr.\ Ace's system.}{fig 6.3}
Mr.\ Ace holds the stock under his system during the days indicated by broken lines.
We note that for the history shown in Figure~\ref{fig 6.3}, his system nets him a gain of 4~dollars.
\par
We have written a program {\bf StockSystem}\index{StockSystem (program)} to simulate the fortune of
Mr.\ Ace if he uses his sytem over an $n$-day period.  If one runs this program a large number of
times, for
$n = 20$, say, one finds that his expected winnings are very close to 0, but the probability that he
is ahead after 20 days is significantly greater than 1/2.  For small values of $n$, the exact
distribution of winnings can be calculated.  The distribution for the case $n = 20$ is shown in
Figure~\ref{fig 6.3.1}.  Using this distribution, it is easy to calculate that
the expected value of his winnings is exactly 0. This is another instance of the fact that a
fair game (a martingale\index{martingale}) remains fair under quite general systems of play.
\putfig{4truein}{PSfig6-3-1}{Winnings distribution for $n = 20$.}{fig 6.3.1}
\par
Although the expected value of his winnings is 0, the probability that Mr.\ Ace is ahead after 20
days is about .610.  Thus, he would be able to tell his friends that his
system gives him a better chance of being ahead than that of someone who simply buys the stock and
holds it, if our simple random model is correct.  There have been a number of studies
to determine how random the stock market is.
\end{example}

\subsection*{Historical Remarks}

%**********Put an account of the St. Petersburg paradox in this section.
With the Law of Large Numbers to bolster the frequency interpretation of probability,
we find it natural to justify the definition of expected value in terms of the average
outcome over a large number of repetitions of the experiment.  The concept of expected
value was used before it was formally defined; and when it was used, it was considered
not as an average value but rather as the appropriate value for a gamble.  For example
recall, from the Historical Remarks section of Chapter~\ref{chp 1}, Section~\ref{sec 1.2}, Pascal's way of finding the value of a three-game series that had to be called
off before it is finished.
\par
Pascal\index{PASCAL, B.} first observed that if each player has only one game to win, then the
stake of 64~pistoles should be divided evenly.  Then he considered the case where one
player has won two games and the other one.

\begin{quote} Then consider, Sir, if the first man wins, he gets 64~pistoles, if he
loses he gets 32.  Thus if they do not wish to risk this last game, but wish to
separate without playing it, the first man must say: ``I am certain to get
32~pistoles, even if I lose I still get them; but as for the other 32~pistoles,
perhaps I will get them, perhaps you will get them, the chances are equal.  Let us
then divide these 32~pistoles in half and give one half to me as well as my 32 which
are mine for sure."  He will then have 48~pistoles and the other 16.\footnote{Quoted
in F.~N. David, \emx {Games, Gods and Gambling} (London: Griffin, 1962), p.~231.}
\end{quote}

Note that Pascal reduced the problem to a symmetric bet in which each player gets the
same amount and takes it as obvious that in this case the stakes should be divided
equally.

The first systematic study of expected value appears in Huygens'\index{HUYGENS, C.|(} book.
Like Pascal, Huygens find the value of a gamble by assuming that the answer is obvious for
certain symmetric situations and uses this to deduce the expected for the general situation.
He does this in steps.  His first proposition is

\begin{quote} Prop.~I. If I expect $a$ or $b$, either of which, with equal
probability, may fall to me, then my Expectation is worth $(a + b)/2$, that is, the
half Sum of
$a$ and $b$.\footnote{C. Huygens, \emx {Calculating in Games of Chance,} translation
attributed to John Arbuthnot (London, 1692), p.~34.}
\end{quote}

Huygens proved this as follows: Assume that two player A and B play a game in which
each player puts up a stake of $(a + b)/2$ with an equal chance of winning the total
stake.  Then the value of the game to each player is $(a + b)/2$.  For example, if
the game had to be called off clearly each player should just get back his original
stake.  Now, by symmetry, this value is not changed if we add the condition that the
winner of the game has to pay the loser an amount $b$ as a consolation prize.  Then
for player A the value is still $(a + b)/2$.  But what are his possible outcomes for
the modified game?  If he wins he gets the total stake $a + b$ and must pay B an
amount $b$ so ends up with $a$.  If he loses he gets an amount $b$ from player B.
Thus player A wins $a$ or $b$ with equal chances and the value to him is $(a + b)/2$.

Huygens illustrated this proof in terms of an example.  If you are offered a game in
which you have an equal chance of winning 2~or~8, the expected value is~5, since this
game is equivalent to the game in which each player stakes 5 and agrees to pay the
loser 3 --- a game in which the value is obviously 5.

Huygens' second proposition is

\begin{quote} Prop.~II. If I expect $a$,~$b$, or~$c$, either of which, with equal
facility, may happen, then the Value of my Expectation is $(a + b + c)/3$, or the
third of the Sum of $a$,~$b$, and~$c$.\footnote{ibid., p.~35.}
\end{quote}

His argument here is similar.  Three players, A,~B, and~C, each stake
$$(a+b+c)/3$$
in a game they have an equal chance of winning.  The value of this game
to player A is clearly the amount he has staked.  Further, this value is not changed if A enters
into an agreement with B that if one of them wins he pays the other a consolation prize
of $b$ and with C that if one of them wins he pays the other a consolation prize of $c$.
By symmetry these agreements do not change the value of the game.  In this modified game,
if A wins he wins the total stake
$a + b + c$ minus the consolation prizes $b + c$ giving him a final winning of
$a$.  If B wins, A wins $b$ and if C wins, A wins $c$.  Thus A finds himself in a
game with value $(a + b + c)/3$ and with outcomes $a$,~$b$, and~$c$ occurring with
equal chance.  This proves Proposition II.
\par
More generally, this reasoning shows that if there are $n$ outcomes
$$a_1,\ a_2,\ \ldots,\ a_n\ ,$$
all occurring with the same probability, the expected value is
$$
\frac {a_1 + a_2 +\cdots+ a_n}n\ .
$$

In his third proposition Huygens considered the case where you win $a$ or $b$ but
with unequal probabilities.  He assumed there are $p$ chances of winning
$a$, and $q$ chances of winning $b$, all having the same probability.  He then showed
that the expected value is
$$ E = \frac p{p + q} \cdot a + \frac q{p + q} \cdot b\ .
$$ This follows by considering an equivalent gamble with $p + q$ outcomes all
occurring with the same probability and with a payoff of $a$ in $p$ of the outcomes
and $b$ in $q$ of the outcomes.  This allowed Huygens to compute the expected value
for experiments with unequal probabilities, at least when these probablities are
rational numbers.
\par
Thus, instead of defining the expected value as a weighted average, Huygens assumed
that the expected value of certain symmetric gambles are known and deduced the other
values from these.  Although this requires a good deal of clever manipulation,
Huygens ended up with values that agree with those given by our modern definition of
expected value.  One advantage of this method is that it gives a justification for
the expected value in cases where it is not reasonable to assume that you can repeat
the experiment a large number of times, as for example, in betting that at least two
presidents died on the same day of the year.  (In fact, three did; all were signers
of the Declaration of Independence, and all three died on July~4.)
\par
In his book, Huygens calculated the expected value of games using techniques similar
to those which we used in computing the expected value for roulette at Monte Carlo.
For example, his proposition XIV is:

\begin{quote} Prop.~XIV. If I were playing with another by turns, with two Dice, on
this Condition, that if I throw 7 I gain, and if he throws 6 he gains allowing him
the first Throw: To find the proportion of my Hazard to his.\footnote{ibid., p.~47.}
\end{quote}
\par A modern description of this game is as follows.  Huygens and his opponent take
turns rolling a die.  The game is over if Huygens rolls a 7 or his opponent rolls a
6.  His opponent rolls first.  What is the probability that Huygens wins the game?
\par  To solve this problem Huygens let
$x$ be his chance of winning when his opponent threw first and $y$ his chance of
winning when he threw first.  Then on the first roll his opponent wins on 5 out of
the 36 possibilities.  Thus,
$$ x = \frac {31}{36} \cdot y\ .
$$ But when Huygens rolls he wins on 6 out of the 36 possible outcomes, and in the
other 30, he is led back to where his chances are $x$.  Thus
$$ y = \frac 6{36} + \frac {30}{36} \cdot x\ .
$$ From these two equations Huygens found that $x = 31/61$.\index{HUYGENS, C.|)}

Another early use of expected value appeared in Pascal's\index{PASCAL, B.} argument to show that a
rational person should believe in the existence of God.\index{existence of God}\footnote{Quoted in
I. Hacking, \emx {The Emergence of Probability} (Cambridge: Cambridge Univ.\ Press,
1975).}  Pascal said that we have to make a wager whether to believe or not to
believe.  Let $p$ denote the probability that God does not exist.  His discussion suggests that we are playing a game with two strategies,
believe and not believe, with payoffs as shown in Table~\ref{table 6.4}.
\begin{table}
\centering
$$\begin{tabular}{ccc}
              & \hspace{.7in}God does not exist & \hspace{.05in}God exists \\
              & \\
              & \hspace{.8in}$p$              & \hspace{.15in}$1 - p$ \\
\end{tabular}
$$
$$\begin{tabular}{l|c|c|}\cline{2-2} \cline{3-3}
 believe      & \hspace{.5in} $-u$\hspace{.5in} &\hspace{.3in} $v\hspace{.3in} $\\
\cline{2-2} \cline{3-3}
 not believe  &    0  & $-x$\\ \cline{2-2} \cline{3-3}
\end{tabular}
$$
\caption{Payoffs.}
\label{table 6.4}
\end{table}

Here $-u$ represents the cost to you of passing up some worldly pleasures as a
consequence of believing that God exists.  If you do not believe, and God is a
vengeful God, you will lose $x$.  If God exists and you do believe you will gain v.
Now to determine which strategy is best you should
compare the two expected values
$$ p(-u) + (1 - p)v \qquad {\rm and} \qquad p0 + (1 - p)(-x),
$$ and choose the larger of the two.  In general, the choice will depend upon the
value of $p$.  But Pascal assumed that the value of $v$ is infinite and so the
strategy of believing is best no matter what probability you assign for the existence
of God.  This example is considered by some to be the beginning of decision theory.
Decision analyses of this kind appear today in many fields, and, in particular, are
an important part of medical diagnostics and corporate business decisions.

Another early use of expected value was to decide the price of annuities.\index{annuity}  The study
of statistics has its origins in the use of the bills of mortality kept in the
parishes in London from 1603.  These records kept a weekly tally of christenings and
burials.  From these John Graunt\index{GRAUNT, J.} made estimates for the population of London and
also provided the first mortality
data,\index{mortality table}\footnote{ibid., p.~108.} shown in Table~\ref{table 6.5}.
\begin{table}
\centering
$$
\begin{tabular}{rr}
    & \\ \hline
 Age &\hspace{.35in} Survivors  \\ \hline
 0    &  100  \\
 6    &   64  \\ 16    &   40  \\ 26    &   25  \\ 36    &   16  \\ 46    &   10  \\
56    &    6  \\ 66    &    3  \\ 76    &    1  \\ \hline
\end{tabular}$$
\caption{Graunt's mortality data.}
\label{table 6.5}
\end{table}

As Hacking observes, Graunt apparently constructed this table by assuming that after
the age of~6 there is a constant probability of about 5/8 of surviving for another
decade.\footnote{ibid., p.~109.}  For example, of the 64~people who survive to age~6,
5/8 of 64~or~40 survive to~16, 5/8 of these 40~or~25 survive to~26, and so forth.  Of
course, he rounded off his figures to the nearest whole person.

Clearly, a constant mortality rate cannot be correct throughout the whole range, and
later tables provided by Halley were more realistic in this respect.\footnote{E.
Halley, ``An Estimate of The Degrees of Mortality of Mankind," \emx {Phil.\ Trans.\
Royal.\ Soc.,} vol.~17 (1693), pp.~596--610; 654--656.}

A \emx {terminal annuity}\index{annuity!terminal} provides a fixed amount of money during a period of
$n$ years.  To determine the price of a terminal annuity one needs only to know the
appropriate interest rate.  A \emx {life annuity}\index{annuity!life} provides a fixed amount during
each year of the buyer's life.  The appropriate price for a life annuity is the
expected value of the terminal annuity evaluated for the random lifetime of the
buyer.  Thus, the work of Huygens in introducing expected value and the work of
Graunt and Halley in determining mortality tables led to a more rational method for
pricing annuities.  This was one of the first serious uses of probability theory
outside the gambling houses.

Although expected value plays a role now in every branch of science, it retains its
importance in the casino.  In 1962, Edward Thorp's\index{THORP, E.} book \emx {Beat the
Dealer}\footnote{E. Thorp, \emx {Beat the Dealer} (New York: Random House, 1962).}
provided the reader with a strategy for playing the popular casino game of
blackjack\index{blackjack} that would assure the player a positive expected winning.  This book
forevermore changed the belief of the casinos that they could not be beat.

\exercises
\begin{LJSItem}

\i\label{exer 6.1.1} A card is drawn at random from a deck consisting of cards
numbered 2 through~10.  A player wins 1~dollar if the number on the card is odd and
loses 1~dollar if the number if even.  What is the expected value of his winnings?

\i\label{exer 6.1.2} A card is drawn at random from a deck of playing cards.  If
it is red, the player wins 1~dollar; if it is black, the player loses 2~dollars.
Find the expected value of the game.

\i\label{exer 6.1.3} In a class there are 20~students: 3 are 5' 6", 5 are 5'8", 4
are 5'10", 4 are 6', and 4 are 6' 2".  A student is chosen at random.  What is the
student's expected height?

\i\label{exer 6.1.4} In Las Vegas the roulette wheel has a~0 and a~00 and then the
numbers 1~to~36 marked on equal slots; the wheel is spun and a ball stops randomly in
one slot.  When a player bets 1~dollar on a number, he receives 36~dollars if the
ball stops on this number, for a net gain of 35~dollars; otherwise, he loses his
dollar bet.  Find the expected value for his winnings.

\i\label{exer 6.1.5} In a second version of roulette in Las Vegas, a player bets
on red or black.  Half of the numbers from~1 to~36 are red, and half are black.  If a
player bets a dollar on black, and if the ball stops on a black number, he gets his
dollar back and another dollar.  If the ball stops on a red number or on~0 or~00 he
loses his dollar.  Find the expected winnings for this bet.

\i\label{exer 6.1.6} A die is rolled twice.  Let $X$ denote the sum of the two
numbers that turn up, and $Y$ the difference of the numbers (specifically, the number
on the first roll minus the number on the second).  Show that $E(XY) = E(X)E(Y)$.  Are
$X$ and $Y$ independent?

\istar\label{exer 6.1.7} Show that, if $X$ and $Y$ are random variables taking on
only two values each, and if $E(XY) = E(X)E(Y)$, then $X$ and $Y$ are independent.

\i\label{exer 6.1.8} A royal family has children until it has a boy or until it
has three children, whichever comes first.  Assume that each child is a boy with
probability 1/2.  Find the expected number of boys in this royal family and the
expected number of girls.

\i\label{exer 6.1.9} If the first roll in a game of craps is neither a natural nor
craps, the player can make an additional bet, equal to his original one, that he will
make his point before a seven turns up.  If his point is four or ten he is paid off
at $2 : 1$ odds; if it is a five or nine he is paid off at odds $3 : 2$; and if it is
a six or eight he is paid off at odds $6 : 5$.  Find the player's expected winnings
if he makes this additional bet when he has the opportunity.

\i\label{exer 6.1.10} In Example~\ref{exam 6.12} assume that Mr. Ace decides to buy the stock
and hold it until it goes up 1~dollar and then sell and not buy again.  Modify the
program {\bf StockSystem} to find the distribution of his profit under this system
after a twenty-day period.  Find the expected profit and the probability that he comes
out ahead.

\i\label{exer 6.1.11} On September~26, 1980, the \emx {New York Times} reported
that a mysterious stranger strode into a Las Vegas casino, placed a single bet of
777{,}000 dollars on the ``don't pass" line at the crap table, and walked away with
more than 1.5 million dollars.  In the ``don't pass" bet, the bettor is essentially
betting with the house.  An exception occurs if the roller rolls a~12 on the first
roll.  In this case, the roller loses and the ``don't pass" better just gets back
the money bet instead of winning.  Show that the ``don't pass" bettor has a more
favorable bet than the roller.

\i\label{exer 6.1.12} Recall that in the \emx {martingale doubling system}\index{martingale
betting system} (see Exercise~\ref{sec 1.1}.\ref{exer 1.1.10}), the player doubles
his bet each time he loses.  Suppose that you are
playing roulette in a \emx {fair casino} where there are no 0's, and you bet on red
each time.  You then win with probability 1/2 each time.  Assume that you enter the casino with
100~dollars, start with a
1-dollar bet and employ the martingale system.  You stop as soon as you have won one bet, or in the unlikely event that black turns up six times in a
row so that you are down 63~dollars and cannot make the required 64-dollar bet.  Find
your expected winnings under this system of play.

\i\label{exer 6.1.13} You have 80~dollars and play the following game.  An urn
contains two white balls and two black balls.  You draw the balls out one at a time
without replacement until all the balls are gone.  On each draw, you bet half of your
present fortune that you will draw a white ball.  What is your expected final fortune?

\i\label{exer 6.1.14} In the hat check problem (see Example~\ref{exam 3.13}), it
was assumed that $N$ people check their hats and the hats are handed
back at random.  Let $X_j = 1$ if the $j$th person gets his or her hat and 0
otherwise.  Find $E(X_j)$ and $E(X_j \cdot X_k)$ for $j$ not equal to $k$.  Are $X_j$
and $X_k$ independent?

\i\label{exer 6.1.16} A box contains two gold balls and three silver balls.  You
are allowed to choose successively balls from the box at random.  You win 1~dollar
each time you draw a gold ball and lose 1~dollar each time you draw a silver ball.
After a draw, the ball is not replaced.  Show that, if you draw until you are ahead
by 1~dollar or until there are no more gold balls, this is a favorable game.

\i\label{exer 6.1.17} Gerolamo Cardano\index{CARDANO, G.} in his book, \emx {The Gambling Scholar,}
written in the early 1500s, considers the following carnival game.  There are six
dice.  Each of the dice has five blank sides.  The sixth side has a number between
1~and~6---a different number on each die.  The six dice are rolled and the player
wins a prize depending on the total of the numbers which turn up.

\begin{enumerate}
\item Find, as Cardano did, the expected total without finding its distribution.

\item Large prizes were given for large totals with a modest fee to play the game.
Explain why this could be done.
\end{enumerate}

\i\label{exer 6.1.18} Let $X$ be the first time that a \emx {failure} occurs in
an infinite sequence of Bernoulli trials with probability $p$ for success.  Let $p_k
= P(X = k)$ for $k = 1$,~2, \dots.  Show that $p_k = p^{k - 1}q$ where $q = 1 - p$.
Show that $\sum_k p_k = 1$.  Show that $E(X) = 1/q$.  What is the expected number of
tosses of a coin required to obtain the first tail?

\i\label{exer 6.1.19} Exactly one of six similar keys opens a certain door.  If
you try the keys, one after another, what is the expected number of keys that you
will have to try before success?

\i\label{exer 6.1.20} A multiple choice exam is given.  A problem has four
possible answers, and exactly one answer is correct.  The student is allowed to
choose a subset of the four possible answers as his answer.  If his chosen subset
contains the correct answer, the student receives three points, but he loses one
point for each wrong answer in his chosen subset.  Show that if he just guesses a
subset uniformly and randomly his expected score is zero.

\i\label{exer 6.1.21} You are offered the following game to play: a fair coin is
tossed until heads turns up for the first time (see Example~\ref{exam 6.055}).  If
this occurs on the first toss you receive 2~dollars, if it occurs on the second toss
you receive $2^2 = 4$ dollars and, in general, if heads turns up for the first time
on the $n$th toss you receive $2^n$ dollars.

\begin{enumerate}
\item Show that the expected value of your winnings does not exist (i.e., is given by
a divergent sum) for this game.  Does this mean that this game is favorable no matter
how much you pay to play it?

\item Assume that you only receive $2^{10}$ dollars if any number greater than or
equal to ten tosses are required to obtain the first head.  Show that your expected
value for this modified game is finite and find its value.

\item Assume that you pay 10~dollars for each play of the original game.  Write a
program to simulate 100 plays of the game and see how you do.

\item Now assume that the utility of $n$ dollars is $\sqrt n$.  Write an expression
for the expected utility of the payment, and show that this expression has a finite
value.  Estimate this value.  Repeat this exercise for the case that the utility
function is $\log(n)$.

\end{enumerate}

\i\label{exer 6.1.100} Let $X$ be a random variable which is
Poisson distributed with parameter $\lambda$.  Show that $E(X) = \lambda$.  \emx {Hint}:
Recall that
$$e^x = 1 + x + \frac{x^2}{2!} + \frac{x^3}{3!} + \cdots\,.$$

\i\label{exer 6.1.22} Recall that in Exercise~\ref{sec 1.1}.\ref{exer 1.1.14}, we
considered a town with two hospitals.\index{hospital}  In the large hospital about 45 babies are
born each day, and in the smaller hospital about 15 babies\index{babies} are born each day.  We were
interested in  guessing which hospital would have on the average the largest number of
days with the property that more than 60 percent of the children born on that day are
boys.  For each hospital find the expected number of days in a year that have the
property that more than 60~percent of the children born on that day were boys.

\i\label{exer 6.1.23} An insurance company has 1{,}000 policies on men of age~50.
The company estimates that the probability that a man of age~50 dies within a year is
.01.  Estimate the number of claims that the company can expect from beneficiaries of
these men within a year.

\i\label{exer 6.1.24} Using the life table for 1981 in Appendix~C, write
a program to compute the expected lifetime for males and females of each possible age
from~1 to~85.  Compare the results for males and females.  Comment on whether life
insurance should be priced differently for males and females.

\istar\label{exer 6.1.25} A deck of ESP\index{ESP} cards consists of 20 cards each of two types:
say ten stars, ten circles (normally there are five types).  The deck is shuffled and
the cards turned up one at a time.  You, the alleged percipient, are to name the
symbol on each card \emx {before} it is turned up.
\par
Suppose that you are really just guessing at the cards.  If you do not get to see
each card after you have made your guess, then it is easy to calculate the expected
number of correct guesses, namely ten.
\par
If, on the other hand, you are guessing with information, that is, if you see each
card after your guess, then, of course, you might expect to get a higher score.  This
is indeed the case, but calculating the correct expectation is no longer easy.
\par
But it is easy to do a computer simulation of this guessing with information, so we
can get a good idea of the expectation by simulation.  (This is similar to the way
that skilled blackjack players make blackjack into a favorable game by observing the
cards that have already been played.  See Exercise~\ref{exer 6.1.29}.)

\begin{enumerate}
\item First, do a simulation of guessing without information, repeating the
experiment at least 1000 times.  Estimate the expected number of correct answers and
compare your result with the theoretical expectation.

\item What is the best strategy for guessing with information?

\item Do a simulation of guessing with information, using the strategy in (b).
Repeat the experiment at least 1000 times, and estimate the expectation in this case.

\item Let $S$ be the number of stars and $C$ the number of circles in the deck.  Let
$h(S,C)$ be the expected winnings using the optimal guessing strategy in (b).  Show
that $h(S,C)$ satisfies the recursion relation
$$ h(S,C) = \frac S{S + C} h(S - 1,C) + \frac C{S + C} h(S,C - 1) + \frac
{\max(S,C)}{S + C}\ ,
$$ and $h(0,0) = h(-1,0) = h(0,-1) = 0$.  Using this relation, write a program to
compute $h(S,C)$ and find $h(10,10)$.  Compare the computed value of $h(10,10)$ with
the result of your simulation in (c).  For more about this exercise and
Exercise~\ref{exer 6.1.26} see Diaconis and Graham.\index{DIACONIS, P.}\index{GRAHAM,
R.}\footnote{P. Diaconis and R. Graham, ``The Analysis of Sequential Experiments with Feedback to
Subjects," \emx {Annals of Statistics,} vol.~9 (1981), pp.~3--23.}
\end{enumerate}

\istar\label{exer 6.1.26} Consider the ESP\index{ESP} problem as described in Exercise~\ref{exer
6.1.25}.  You are again guessing with information, and you are using the optimal
guessing strategy of guessing \emx {star} if the remaining deck has more stars, {\em
circle} if more circles, and tossing a coin if the number of stars and circles are
equal.  Assume that $S \geq C$, where $S$ is the number of stars and $C$ the number
of circles.
\par We can plot the results of a typical game on a graph, where the horizontal axis
represents the number of steps and the vertical axis represents the \emx {difference}
between the number of stars and the number of circles that have been turned up.  A
typical game is shown in Figure~\ref{fig 6.4}.  In this particular game, the order in
which the cards were turned up is $(C,S,S,S,S,C,C,S,S,C)$.  Thus, in this particular
game, there were six stars and four circles in the deck.  This means, in particular,
that every game played with this deck would have a graph which ends at the point
$(10, 2)$.  We define the line $L$ to be the horizontal line which goes through the
ending point on the graph (so its vertical coordinate is just the difference between
the number of stars and circles in the deck).

\putfig{4truein}{PSfig6-4}{Random walk for ESP.}{fig 6.4}

\begin{enumerate}
\item Show that, when the random walk is below the line $L$, the player guesses right
when the graph goes up (star is turned up) and, when the walk is above the line, the
player guesses right when the walk goes down (circle turned up).  Show from this
property that the subject is sure to have at least $S$ correct guesses.

\item When the walk is at a point $(x,x)$ \emx {on} the line $L$ the number of stars
and circles remaining is the same, and so the subject tosses a coin.  Show that the
probability that the walk reaches $(x,x)$ is
$$
\frac{{S \choose x}{C \choose x}}{{{S + C} \choose {2x}}}\ .
$$ \emx {Hint}: The outcomes of $2x$ cards is a hypergeometric distribution (see
Section~\ref{sec 5.1}).

\item Using the results of (a) and (b) show that the expected number of correct
guesses under intelligent guessing is
$$ S + \sum_{x = 1}^C \frac12 \frac{{S \choose x}{C \choose x}}{{{S + C} \choose
{2x}}}\ .
$$
\end{enumerate}

\i\label{exer 6.1.27} It has been said\footnote{J.~F. Box,  \emx {R.~A. Fisher, The
Life of a Scientist} (New York: John Wiley and Sons, 1978).} that a Dr.~B.~Muriel
Bristol declined a cup of tea\index{tea} stating that she preferred a cup into which
milk\index{milk} had been poured first.  The famous statistician R.~A. Fisher\index{FISHER, R. A.}
carried out a test to see if she could tell whether milk was put in before or after the tea.
Assume that for the test Dr.~Bristol was given eight cups of tea---four in which the milk was put in
before the tea and four in which the milk was put in after the tea.
% (cf. Exercise~\ref{sec 3.2}.\ref{exer 3.2.8}).

\begin{enumerate}
\item What is the expected number of correct guesses the lady would make if she had
no information after each test and was just guessing?

\item Using the result of Exercise~\ref{exer 6.1.26} find the expected number of
correct guesses if she was told the result of each guess and used an optimal guessing
strategy.
\end{enumerate}

\i\label{exer 6.1.28} In a popular computer game the computer picks an integer
from~1 to~$n$ at random.  The player is given $k$ chances to guess the number.  After
each guess the computer responds ``correct," ``too small," or ``too big."

\begin{enumerate}
\item Show that if $n \leq 2^k - 1$, then there is a strategy that guarantees you
will correctly guess the number in $k$ tries.

\item Show that if $n \geq 2^k - 1$, there is a strategy that assures you of
identifying one of $2^k - 1$ numbers and hence gives a probability of $(2^k - 1)/n$
of winning.  Why is this an optimal strategy?  Illustrate your result in terms of the
case $n = 9$ and $k = 3$.
\end{enumerate}

\i\label{exer 6.1.29} In the casino game of blackjack\index{blackjack} the dealer is dealt two
cards, one face up and one face down, and each player is dealt two cards, both face
down.  If the dealer is showing an ace the player can look at his down cards and then
make a bet called an  \emx {insurance} bet.  (Expert players will recognize why it is
called insurance.)  If you make this bet you will win the bet if the dealer's second
card is a \emx {ten card}: namely, a ten, jack, queen, or king.  If you win, you are
paid twice your insurance bet; otherwise you lose this bet.  Show that, if the only
cards you can see are the dealer's ace and your two cards and if your cards are not
ten cards, then the insurance bet is an unfavorable bet.  Show, however, that if you
are playing two hands simultaneously, and you have no ten cards, then it is a
favorable bet.  (Thorp\index{THORP, E.}\footnote{E.~Thorp,  \emx {Beat the Dealer} (New York: Random
House, 1962).} has shown that the game of blackjack is favorable to the player if he or she can keep
good enough track of the cards that have been played.)

\i\label{exer 6.1.30} Assume that, every time you buy a box of Wheaties\index{Wheaties}, you
receive a picture of one of the $n$ players for the New York Yankees\index{New York Yankees} (see
Exercise~\ref{sec 3.2}.\ref{exer 3.2.34}).  Let $X_k$ be the
number of additional boxes you have to buy, after you have obtained $k - 1$ different
pictures, in order to obtain the next new picture.  Thus $X_1 = 1$, $X_2$ is the number
of boxes bought after this to obtain a picture different from the first pictured
obtained, and so forth.

\begin{enumerate}
\item Show that $X_k$ has a geometric distribution with $p = (n - k + 1)/n$.

\item Simulate the experiment for a team with 26 players (25 would be more accurate
but we want an even number).  Carry out a number of simulations and estimate the
expected time required to get the first 13 players and the expected time to get the
second 13.  How do these expectations compare?

\item Show that, if there are $2n$ players, the expected time to get the first half
of the players is
$$ 2n \left( \frac 1{2n} + \frac 1{2n - 1} +\cdots+ \frac 1{n + 1} \right)\ ,
$$ and the expected time to get the second half is
$$ 2n \left( \frac 1n + \frac 1{n - 1} +\cdots+ 1 \right)\ .
$$

\item In Example~\ref{exam 6.5} we stated that
$$ 1 + \frac 12 + \frac 13 +\cdots+ \frac 1n \sim \log n + .5772 + \frac 1{2n}\ .
$$ Use this to estimate the expression in (c).  Compare these estimates with the
exact values and also with your estimates obtained by simulation for the case $n =
26$.
\end{enumerate}

\istar\label{exer 6.1.31} (Feller\index{FELLER, W.}\footnote{W. Feller,  \emx {Introduction to
Probability Theory and Its Applications,} 3rd ed., vol.~1 (New York: John Wiley and
Sons, 1968), p.~240.})  A large number, $N$, of people are subjected to a blood\index{blood test}
test.  This can be administered in two ways: (1)~Each person can be tested separately, in
this case $N$ test are required, (2)~the blood samples of $k$ persons can be pooled
and analyzed together.  If this test is  \emx {negative,} this one test suffices for
the $k$ people.  If the test is  \emx {positive,} each of the $k$ persons must be
tested separately, and in all, $k + 1$ tests are required for the $k$ people.  Assume
that the probability $p$ that a test is positive is the same for all people and that
these events are independent.

\begin{enumerate}
\item Find the probability that the test for a pooled sample of $k$ people will be
positive.

\item What is the expected value of the number $X$ of tests necessary under
plan~(2)?  (Assume that $N$ is divisible by $k$.)

\item For small $p$, show that the value of $k$ which will minimize the expected
number of tests under the second plan is approximately $1/\sqrt p$.
\end{enumerate}

\i\label{exer 6.1.32} Write a program to add random numbers chosen from $[0,1]$
until the first time the sum is greater than one.  Have your program repeat this
experiment a number of times to estimate the expected number of selections necessary
in order that the sum of the chosen numbers first exceeds~1.  On the basis of your
experiments, what is your estimate for this number?

\istar\label{exer 6.1.33} The following related discrete problem also gives a good
clue for the answer to Exercise~\ref{exer 6.1.32}.  Randomly select with replacement
$t_1$,~$t_2$,
\dots,~$t_r$ from the set $(1/n, 2/n, \dots, n/n)$.  Let $X$ be the smallest value of
$r$ satisfying
$$ t_1 + t_2 +\cdots+ t_r > 1\ .
$$ Then $E(X) = (1 + 1/n)^n$.  To prove this, we can just as well choose
$t_1$,~$t_2$, \dots,~$t_r$ randomly with replacement from the set $(1, 2,
\dots, n)$ and let $X$ be the smallest value of $r$ for which
$$ t_1 + t_2 +\cdots+ t_r > n\ .
$$
\begin{enumerate}
\item Use Exercise~\ref{sec 3.2}.\ref{exer 3.2.35.5} to show that
$$ P(X \geq j + 1) = {n \choose j}{\Bigl(\frac {1}{n}\Bigr)^j}\ .
$$
\item Show that
$$ E(X) = \sum_{j = 0}^n P(X \geq j + 1)\ .
$$
\item From these two facts, find an expression for $E(X)$.  This proof is due to
Harris Schultz.\index{SCHULTZ, H.}\footnote{H. Schultz, ``An Expected Value Problem," \emx {Two-Year
Mathematics Journal,} vol.~10, no.~4 (1979), pp.~277--78.}
\end{enumerate}

\istar\label{exer 6.1.34} (Banach's Matchbox\index{Banach's Matchbox}\footnote{W.~Feller, {\em
Introduction to Probability Theory,} vol. 1, p.~166.}) A man carries in each of his two front
pockets a box of matches originally containing $N$ matches.  Whenever he needs a match, he chooses
a pocket at random and removes one from that box.  One day he reaches into a pocket
and finds the box empty.

\begin{enumerate}
\item Let $p_r$ denote the probability that the other pocket contains $r$ matches.
Define a sequence of  \emx {counter} random variables as follows: Let
$X_i = 1$ if the $i$th draw is from the left pocket, and 0 if it is from the right
pocket.  Interpret $p_r$ in terms of $S_n = X_1 + X_2 +\cdots+ X_n$.  Find a binomial
expression for $p_r$.

\item Write a computer program to compute the $p_r$, as well as the probability that
the other pocket contains at least $r$ matches, for $N = 100$ and $r$ from~0 to~50.

\item Show that $(N - r)p_r = (1/2)(2N + 1)p_{r + 1} - (1/2)(r + 1)p_{r + 1}$\ .

\item Evaluate $\sum_r p_r$.

\item Use (c) and (d) to determine the expectation $E$ of the distribution
$\{p_r\}$.

\item Use Stirling's formula to obtain an approximation for $E$.  How many matches
must each box contain to ensure a value of about 13 for the expectation
$E$?  (Take $\pi = 22/7$.)
\end{enumerate}

\i\label{exer 6.1.35} A coin is tossed until the first time a head turns up.  If
this occurs on the $n$th toss and $n$ is odd you win $2^n/n$, but if $n$ is even
then you lose
$2^n/n$.  Then if your expected winnings exist they are given by the convergent series
$$ 1 - \frac 12 + \frac 13 - \frac 14 +\cdots
$$ called the alternating  \emx {harmonic series.}  It is tempting to say that this
should be the expected value of the experiment.  Show that if we were to do this, the
expected value of an experiment would depend upon the order in which the outcomes are
listed.

\i\label{exer 6.1.37} Suppose we have an urn containing $c$
yellow balls and $d$ green balls.  We draw $k$ balls, without replacement,
from the urn.  Find the expected number of yellow balls drawn.  \emx {Hint}:  Write the
number of yellow balls drawn as the sum of $c$ random variables.

\i\label{exer 6.1.38} The reader is referred to Example~\ref{exam 6.7} for an explanation
of the various options available in Monte Carlo roulette.
\begin{enumerate}
\item Compute the expected winnings of a 1 franc bet on red under option (a).
\item Repeat part (a) for option (b).
\item Compare the expected winnings for all three options.
\end{enumerate}

\istar\label{exer 6.1.39}
(from Pittel\footnote{B. Pittel, Problem \#1195, \emx {Mathematics Magazine,} vol.~58,
no.\ 3 (May 1985), pg.~183.})\index{PITTEL, B.} Telephone books\index{telephone books}, $n$ in
number, are kept in a stack.  The probability that the book numbered $i$ (where $1 \le i \le
n$) is consulted for a given phone call is $p_i > 0$, where the $p_i$'s sum to 1.  After a
book is used, it is placed at the top of the stack.  Assume that the calls are independent
and evenly spaced, and that the system has been employed indefinitely far into the past.  Let
$d_i$ be the average depth of book $i$ in the stack.  Show that $d_i \le d_j$ whenever $p_i
\ge p_j$.  Thus, on the average, the more popular books have a tendency to be closer to the
top of the stack. \emx {Hint}:  Let $p_{ij}$ denote the probability that book $i$ is above
book $j$.  Show that $p_{ij} = p_{ij}(1 - p_j) + p_{ji}p_i$.

\istar\label{exer 6.1.40}
(from Propp\footnote{J. Propp, Problem \#1159, \emx {Mathematics Magazine} vol.~57, no.\ 1
(Feb. 1984), pg.~50.})\index{PROPP, J.} In the previous problem, let $P$ be the probability
that at the present time, each book is in its proper place, i.e., book $i$ is $i$th from the
top.  Find a formula for $P$ in terms of the $p_i$'s.  In addition, find the least upper
bound on $P$, if the $p_i$'s are allowed to vary.  \emx {Hint}:   First find the probability
that book 1 is in the right place.  Then find the probability that book 2 is in the right
place, given that book 1 is in the right place.  Continue.

\istar\label{exer 6.1.41}
(from H. Shultz and B. Leonard\footnote{H. Shultz and B. Leonard, ``Unexpected Occurrences of the
Number $e$,"
\emx {Mathematics Magazine} vol.~62, no. 4 (October, 1989), pp.~269-271.})\index{SHULTZ,
H.}\index{LEONARD, B.}  A sequence of random numbers in $[0, 1)$ is generated until the sequence is
no longer monotone increasing.  The numbers are chosen according to the uniform distribution.  What
is the expected length of the sequence?  (In calculating the length, the term that destroys
monotonicity is included.) \emx {Hint}:  Let $a_1,\ a_2,\ \ldots$ be the sequence and let $X$ denote
the length of the sequence.  Then
$$P(X > k) = P(a_1 < a_2 < \cdots < a_k)\ ,$$
and the probability on the right-hand side is easy to calculate.  Furthermore, one can show that
$$E(X) = 1 + P(X > 1) + P(X > 2) + \cdots\ .$$

\i\label{exer 6.1.42}
Let $T$ be the random variable that counts the number of 2-unshuffles performed on an $n$-card deck
until all of the labels on the cards are distinct.  This random variable was discussed in
Section~\ref{sec 3.3}.  Using Equation~\ref{eq 3.3.1} in that section, together with the formula
$$
E(T) = \sum_{s = 0}^\infty P(T > s)
$$
that was proved in Exercise~\ref{exer 6.1.33}, show that
$$
E(T) = \sum_{s = 0}^\infty \left(1 - {{2^s}\choose n}\frac {n!}{2^{sn}}\right)\ .
$$
Show that for $n = 52$, this expression is approximately equal to 11.7.  (As was stated in
Chapter~\ref{chp 3}, this means that on the average, almost 12 riffle shuffles of a 52-card
deck are required in order for the process to be considered random.)

\end{LJSItem}

\section{Variance of Discrete Random Variables}\label{sec 6.2}

The usefulness of the expected value as a prediction for the outcome of an experiment
is increased when the outcome is not likely to deviate too much from the expected
value.  In this section we shall introduce a measure of this deviation, called the
variance.

\subsection*{Variance}

%**********Some comments we made on pg. 238 (prompted by Mark) have not been acted
%upon yet.

\begin{definition}\label{def 6.4} Let $X$ be a numerically valued random variable
with expected value $\mu = E(X)$.  Then the  \emx {variance}\index{variance} of $X$, denoted by
$V(X)$, is
$$ V(X) = E((X - \mu)^2)\ .
$$
\end{definition} Note that, by Theorem~\ref{thm 6.3.5}, $V(X)$ is given by

\begin{equation} V(X) = \sum_x (x - \mu)^2 m(x)\ ,
\label{eq 6.1}
\end{equation} where $m$ is the distribution function of $X$.

\subsection*{Standard Deviation}

The  \emx {standard deviation}\index{standard deviation} of $X$, denoted by $D(X)$, is $D(X) =
\sqrt {V(X)}$.  We often write $\sigma$ for $D(X)$ and $\sigma^2$ for $V(X)$.

\begin{example}\label{exam 6.13} Consider one roll of a die.  Let $X$ be the number
that turns up.  To find
$V(X)$, we must first find the expected value of $X$.  This is
\begin{eqnarray*}
\mu & = & E(X) = 1\Bigl(\frac 16\Bigr) + 2\Bigl(\frac 16\Bigr) + 3\Bigl(\frac
16\Bigr) +  4\Bigl(\frac 16\Bigr) + 5\Bigl(\frac 16\Bigr) + 6\Bigl(\frac 16\Bigr) \\
    & = & \frac 72\ .
\end{eqnarray*}

To find the variance of $X$, we form the new random variable $(X - \mu)^2$ and
compute its expectation.  We can easily do this using the following table.

\begin{table}
\centering
$$\begin{tabular}{ccc}
  && \\ \hline
$x$ & $m(x)$ & $(x - 7/2)^2$ \\ \hline 1 & 1/6 &       25/4  \\ 2 & 1/6
&\hspace{.05in}       9/4  \\ 3 & 1/6 &\hspace{.05in}       1/4  \\ 4 & 1/6
&\hspace{.05in}       1/4  \\ 5 & 1/6 &\hspace{.05in}       9/4  \\ 6 & 1/6 &
25/4  \\\hline
\end{tabular}
$$
\caption{Variance calculation.}
\label{table 6.6}
\end{table}

From this table we find $E((X - \mu)^2)$ is
\begin{eqnarray*} V(X) & = & \frac 16 \left( \frac {25}4 + \frac 94 + \frac 14 +
\frac 14 + \frac 94 + \frac {25}4 \right) \\
     & = &\frac {35}{12}\ ,
\end{eqnarray*} and the standard deviation $D(X) = \sqrt{35/12} \approx 1.707$.
\end{example}

\subsection*{Calculation of Variance}\index{variance!calculation of}

We next prove a theorem that gives us a useful alternative form for computing the
variance.

\begin{theorem}\label{thm 6.7} If $X$ is any random variable with $E(X) = \mu$, then
$$ V(X) = E(X^2) - \mu^2\ .
$$
\proof We have
\begin{eqnarray*} V(X) & = & E((X - \mu)^2) = E(X^2 - 2\mu X + \mu^2) \\
     & = & E(X^2) - 2\mu E(X) + \mu^2 = E(X^2) - \mu^2\ .
\end{eqnarray*}
\end{theorem}

Using Theorem~\ref{thm 6.7}, we can compute the variance of the outcome of a roll of
a die by first computing
\begin{eqnarray*} E(X^2) & = & 1\Bigl(\frac 16\Bigr) + 4\Bigl(\frac 16\Bigr) +
9\Bigl(\frac 16\Bigr) + 16\Bigl(\frac 16\Bigr) + 25\Bigl(\frac 16\Bigr) +
36\Bigl(\frac 16\Bigr) \\
       & = &\frac {91}6\ ,
\end{eqnarray*} and,
$$
V(X)  =  E(X^2) - \mu^2 = \frac {91}{6} - \Bigl(\frac 72\Bigr)^2
 = \frac {35}{12}\ ,
$$
in agreement with the value obtained directly from the definition of
$V(X)$.

\subsection*{Properties of Variance}

The variance has properties very different from those of the expectation.  If
$c$ is any constant, $E(cX) = cE(X)$ and $E(X + c) = E(X) + c$.  These two statements
imply that the expectation is a linear function.  However, the variance is not
linear, as seen in the next theorem.

\begin{theorem}\label{thm 6.6} If $X$ is any random variable and $c$ is any constant,
then
$$ V(cX)  = c^2 V(X)
$$ and
$$ V(X + c) = V(X)\ .
$$
\proof Let $\mu = E(X)$.  Then $E(cX) = c\mu$, and
\begin{eqnarray*} V(cX) &=& E((cX - c\mu)^2) = E(c^2(X - \mu)^2) \\
      &=& c^2 E((X - \mu)^2) = c^2 V(X)\ .
\end{eqnarray*}

To prove the second assertion, we note that, to compute $V(X + c)$, we would replace
$x$ by $x + c$ and $\mu$ by $\mu + c$ in Equation~\ref{eq 6.1}. Then the
$c$'s would cancel, leaving $V(X)$.
\end{theorem}

We turn now to some general properties of the variance.  Recall that if $X$ and
$Y$ are any two random variables, $E(X + Y) = E(X) + E(Y)$.  This is not always true
for the case of the variance.  For example, let $X$ be a random variable with
$V(X) \ne 0$, and define $Y = -X$.  Then $V(X) = V(Y)$, so that $V(X) + V(Y) =
2V(X)$.  But
$X + Y$ is always 0 and hence has variance 0.  Thus $V(X + Y) \ne V(X) + V(Y)$.
\par In the important case of mutually independent random variables, however,  \emx {the
variance of the sum is the sum of the variances.}

\begin{theorem}\label{thm 6.8} Let $X$ and $Y$ be two  \emx {independent} random
variables.  Then
$$V(X + Y) = V(X) + V(Y)\ .$$
\proof Let $E(X) = a$ and $E(Y) = b$.  Then
\begin{eqnarray*} V(X + Y) & = & E((X + Y)^2) - (a + b)^2 \\
         & = & E(X^2) + 2E(XY) + E(Y^2) - a^2 - 2ab - b^2\ .
\end{eqnarray*} Since $X$ and $Y$ are independent, $E(XY) = E(X)E(Y) = ab$.  Thus,
$$ V(X + Y) = E(X^2) - a^2 + E(Y^2) - b^2 = V(X) + V(Y)\ .
$$
\end{theorem}

It is easy to extend this proof, by mathematical induction, to show that {\em
the variance of the sum of any number of mutually independent random variables is the sum
of the individual variances.}  Thus we have the following theorem.

\begin{theorem}\label{thm 6.9} Let $X_1$,~$X_2$, \dots,~$X_n$ be an independent
trials process with $E(X_j) =
\mu$ and $V(X_j) = \sigma^2$.  Let
$$ S_n = X_1 + X_2 +\cdots+ X_n
$$ be the sum, and
$$ A_n = \frac {S_n}n
$$ be the average.  Then
\begin{eqnarray*} E(S_n) &=& n\mu\ , \\
V(S_n) &=& n\sigma^2\ , \\
\sigma(S_n) &=& \sigma \sqrt{n}\ , \\
E(A_n) &=& \mu\ , \\
V(A_n) &=& \frac {\sigma^2}\ , \\
\sigma(A_n) &=& \frac{\sigma}{\sqrt n}\ .
\end{eqnarray*}
\proof Since all the random variables $X_j$ have the same expected value, we have
$$ E(S_n) = E(X_1) +\cdots+ E(X_n) = n\mu\ ,
$$
$$ V(S_n) = V(X_1) +\cdots+ V(X_n) = n\sigma^2\ ,
$$
and
$$\sigma(S_n) = \sigma \sqrt{n}\ .$$

We have seen that, if we multiply a random variable $X$ with mean $\mu$ and variance
$\sigma^2$ by a constant $c$, the new random variable has expected value $c\mu$ and
variance $c^2\sigma^2$.  Thus,
$$ E(A_n) = E\left(\frac {S_n}n \right) = \frac {n\mu}n = \mu\ ,
$$ and
$$ V(A_n) = V\left( \frac {S_n}n \right) = \frac {V(S_n)}{n^2} = \frac
{n\sigma^2}{n^2} = \frac {\sigma^2}n\ .
$$
Finally, the standard deviation of $A_n$ is given by
$$\sigma(A_n) = \frac {\sigma}{\sqrt n}\ .$$
\end{theorem}

The last equation in the above theorem implies that in an independent trials process,
if the individual summands have finite variance, then the standard deviation of the
average goes to 0 as $n \rightarrow \infty$.  Since the standard deviation tells us
something about the spread of the distribution around the mean, we see that for large
values of $n$, the value of $A_n$  is usually very close to the mean of $A_n$, which
equals $\mu$, as shown above.  This statement is made precise in Chapter~\ref{chp 8},
where it is called the Law of Large Numbers.  For example, let $X$ represent the roll
of a fair die.  In Figure~\ref{fig 6.4.5}, we show the distribution of a random variable
$A_n$ corresponding to $X$, for $n = 10$ and $n = 100$.

\putfig{5truein}{PSfig6-4-5}
{Empirical distribution of $A_n$.}{fig 6.4.5}

\begin{example}\label{exam 6.14} Consider $n$ rolls of a die.  We have seen that, if
$X_j$ is the outcome if the
$j$th roll, then $E(X_j) = 7/2$ and $V(X_j) = 35/12$.  Thus, if $S_n$ is the sum of
the outcomes, and $A_n = S_n/n$ is the average of the outcomes, we have
$E(A_n) = 7/2$ and $V(A_n) = (35/12)/n$.  Therefore, as $n$ increases, the expected
value of the average remains constant, but the variance tends to~0.  If the variance
is a measure of the expected deviation from the mean this would indicate that, for
large~$n$, we can expect the average to be very near the expected value.  This is in
fact the case, and we shall justify it in Chapter~\ref{chp 8}.
\end{example}

\subsection*{Bernoulli Trials}

Consider next the general Bernoulli trials process.  As usual, we let $X_j = 1$ if
the $j$th outcome is a success and 0 if it is a failure.  If $p$ is the probability
of a success, and $q = 1 - p$, then
\begin{eqnarray*} E(X_j) & = & 0q + 1p = p\ , \\ E(X_j^2) & = & 0^2q + 1^2p = p\ ,
\end{eqnarray*} and
$$ V(X_j) = E(X_j^2) - (E(X_j))^2 = p - p^2 = pq\ .
$$

Thus, for Bernoulli trials, if $S_n = X_1 + X_2 +\cdots+ X_n$ is the number of
successes, then $E(S_n) = np$, $V(S_n) = npq$, and $D(S_n) = \sqrt{npq}.$  If
$A_n = S_n/n$ is the average number of successes, then $E(A_n) = p$, $V(A_n) = pq/n$,
and $D(A_n) = \sqrt{pq/n}$.  We see that the expected proportion of successes remains
$p$ and the variance tends to~0.  This suggests that the frequency interpretation of
probability is a correct one.  We shall make this more precise in Chapter~\ref{chp 8}.

\begin{example}\label{exam 6.15} Let $T$ denote the number of trials until the first
success in a Bernoulli trials process.  Then $T$ is geometrically distributed.  What
is the variance of $T$?  In Example~\ref{exam 5.7}, we saw that
$$ m_T = \pmatrix{1 &  2 &    3 & \cdots \cr p & qp & q^2p & \cdots \cr}.
$$ In Example~\ref{exam 6.8}, we showed that
$$E(T) = 1/p\ .$$
Thus,
$$V(T) = E(T^2) - 1/p^2\ ,$$
so we need only find
\begin{eqnarray*} E(T^2) & = & 1p + 4qp + 9q^2p + \cdots \\
       & = & p(1 + 4q + 9q^2 + \cdots )\ .
\end{eqnarray*} To evaluate this sum, we start again with
$$ 1 + x + x^2 +\cdots= \frac 1{1 - x}\ .
$$ Differentiating, we obtain
$$ 1 + 2x + 3x^2 +\cdots= \frac 1{(1 - x)^2}\ .
$$ Multiplying by $x$,
$$ x + 2x^2 + 3x^3 +\cdots= \frac x{(1 - x)^2}\ .
$$ Differentiating again gives
$$ 1 + 4x + 9x^2 +\cdots= \frac {1 + x}{(1 - x)^3}\ .
$$ Thus,
$$ E(T^2) = p\frac {1 + q}{(1 - q)^3} = \frac {1 + q}{p^2}
$$ and
\begin{eqnarray*} V(T) & = & E(T^2) - (E(T))^2 \\
     & = & \frac {1 + q}{p^2} - \frac 1{p^2} = \frac q{p^2}\ .
\end{eqnarray*}

For example, the variance for the number of tosses of a coin until the first head
turns up is $(1/2)/(1/2)^2 = 2$.  The variance for the number of rolls of a die until
the first six turns up is $(5/6)/(1/6)^2 = 30$.  Note that, as $p$ decreases, the
variance increases rapidly.  This corresponds to the increased spread of the
geometric distribution as $p$ decreases (noted in Figure~\ref{fig 5.4}).
\end{example}

\subsection*{Poisson Distribution}\index{Poisson distribution!variance of}

Just as in the case of expected values, it is easy to guess the variance of the
Poisson distribution with parameter $\lambda$.  We recall that the variance of a
binomial distribution with parameters $n$ and $p$ equals $npq$.  We also recall that
the Poisson distribution could be obtained as a limit of binomial distributions, if
$n$ goes to $\infty$ and $p$ goes to 0 in such a way that their product is kept fixed at
the value $\lambda$.  In this case, $npq = \lambda q$ approaches $\lambda$, since
$q$ goes to 1.  So, given a Poisson distribution with parameter $\lambda$, we should
guess that its variance is $\lambda$.  The reader is asked to show this in
Exercise~\ref{exer 6.2.100}.

\exercises
\begin{LJSItem}

\i\label{exer 6.2.1} A number is chosen at random from the set $S = \{-1,0,1\}$.
Let $X$ be the number chosen.  Find the expected value, variance, and standard
deviation of $X$.

\i\label{exer 6.2.2} A random variable $X$ has the distribution
$$ p_X = \pmatrix{ 0 & 1 & 2 & 4 \cr 1/3 & 1/3 & 1/6 & 1/6 \cr}\ .
$$ Find the expected value, variance, and standard deviation of $X$.

\i\label{exer 6.2.3} You place a 1-dollar bet on the number 17 at Las Vegas, and
your friend places a 1-dollar bet on black (see Exercises~\ref{sec 1.1}.\ref{exer
1.1.6}~and~\ref{sec 1.1}.\ref{exer 1.1.7}).  Let $X$ be your
winnings and
$Y$ be her winnings.  Compare
$E(X)$,~$E(Y)$, and $V(X)$,~$V(Y)$.  What do these computations tell you about the
nature of your winnings if you and your friend make a sequence of bets, with you
betting each time on a number and your friend betting on a color?

\i\label{exer 6.2.4} $X$ is a random variable with $E(X) = 100$ and $V(X) = 15$.
Find
\begin{enumerate}
\item $E(X^2)$.

\item $E(3X + 10)$.

\item $E(-X)$.

\item $V(-X)$.

\item $D(-X)$.
\end{enumerate}

\i\label{exer 6.2.5} In a certain manufacturing process, the (Fahrenheit)
temperature never varies by more than $2^\circ$ from $62^\circ$.  The temperature is,
in fact, a random variable $F$ with distribution
$$ P_F = \pmatrix{ 60 & 61 & 62 & 63 & 64 \cr 1/10 & 2/10 & 4/10 & 2/10 & 1/10 \cr}\ .
$$
\begin{enumerate}
\item Find $E(F)$ and $V(F)$.
\item Define $T = F - 62$.  Find $E(T)$ and $V(T)$, and compare these answers with
those in part (a).

\item It is decided to report the temperature readings on a Celsius scale, that is,
$C = (5/9)(F - 32)$.  What is the expected value and variance for the readings now?
\end{enumerate}

\i\label{exer 6.2.6} Write a computer program to calculate the mean and variance
of a distribution which you specify as data.  Use the program to compare the
variances for the following densities, both having expected value~0:
$$  p_X = \pmatrix{ -2 & -1 & 0 & 1 & 2 \cr 3/11 & 2/11 & 1/11 & 2/11 & 3/11 \cr}\ ;
$$
$$ p_Y = \pmatrix{ -2 & -1 & 0 & 1 & 2 \cr 1/11 & 2/11 & 5/11 & 2/11 & 1/11 \cr}\ .
$$

\i\label{exer 6.2.7} A coin is tossed three times.  Let $X$ be the number of heads
that turn up.  Find $V(X)$ and $D(X)$.

\i\label{exer 6.2.8} A random sample of 2400 people are asked if they favor a
government proposal to develop new nuclear power plants.  If 40~percent of the people
in the country are in favor of this proposal, find the expected value and the
standard deviation for the number $S_{2400}$ of people in the sample who favored the
proposal.

\i\label{exer 6.2.9} A die is loaded so that the probability of a face coming up is
proportional to the number on that face.  The die is rolled with outcome~$X$.  Find
$V(X)$ and $D(X)$.

\i\label{exer 6.2.10} Prove the following facts about the standard deviation.
\begin{enumerate}
\item $D(X + c) = D(X)$.

\item $D(cX) = |c|D(X)$.
\end{enumerate}

\i\label{exer 6.2.11} A number is chosen at random from the integers 1,~2,~3,
\dots,~$n$.  Let
$X$ be the number chosen.  Show that $E(X) = (n + 1)/2$ and $V(X) = (n - 1)(n +
1)/12$.   \emx {Hint}:  The following identity may be useful:
$$ 1^2 + 2^2 + \cdots + n^2 = \frac{(n)(n+1)(2n+1)}{6}\ .
$$

\i\label{exer 6.2.13} Let $X$ be a random variable with $\mu = E(X)$ and
$\sigma^2 = V(X)$.  Define $X^* = (X - \mu)/\sigma$.  The random variable $X^*$ is
called the  \emx {standardized random variable}\index{standardized random variable} associated with
$X$.  Show that this standardized random variable has expected value 0 and variance 1.

\i\label{exer 6.2.14} Peter and Paul play Heads or Tails (see Example~\ref{exam
1.3}).  Let
$W_n$ be Peter's winnings after $n$ matches.  Show that $E(W_n) = 0$ and
$V(W_n) = n$.

\i\label{exer 6.2.15} Find the expected value and the variance for the number of
boys and the number of girls in a royal family that has children until there is a boy
or until there are three children, whichever comes first.

\i\label{exer 6.2.16} Suppose that $n$ people have their hats returned at random.
Let
$X_i = 1$ if the $i$th person gets his or her own hat back and 0 otherwise.  Let
$S_n =
\sum_{i = 1}^n X_i$.  Then $S_n$ is the total number of people who get their own hats
back.  Show that
\begin{enumerate}
\item $E(X_i^2) = 1/n$.

\item $E(X_i \cdot X_j) = 1/n(n - 1)$ for $i \ne j$.

\item $E(S_n^2) = 2$ (using (a) and (b)).

\item $V(S_n) = 1$.
\end{enumerate}

\i\label{exer 6.2.17} Let $S_n$ be the number of successes in $n$ independent
trials.  Use the program {\bf BinomialProbabilities} (Section~\ref{sec 3.2}) to compute,
for given $n$,~$p$, and~$j$, the probability
$$ P(-j\sqrt{npq} < S_n - np < j\sqrt{npq})\ .
$$
\begin{enumerate}
\item Let $p = .5$, and compute this probability for $j = 1$,~2,~3 and $n =
10$,~30,~50.  Do the same for $p = .2$.

\item Show that the  \emx {standardized random variable} $S_n^* = (S_n -
np)/\sqrt{npq}$ has expected value 0 and variance 1.  What do your results from (a)
tell you about this standardized quantity $S_n^*$?
\end{enumerate}

\i\label{exer 6.2.18} Let $X$ be the outcome of a chance experiment with $E(X) =
\mu$ and $V(X) = \sigma^2$.  When $\mu$ and $\sigma^2$ are unknown, the statistician
often estimates them by repeating the experiment $n$ times with outcomes
$x_1$,~$x_2$, \dots,~$x_n$, estimating $\mu$ by the  \emx {sample mean}\index{sample mean}
$$
\bar{x} = \frac 1n \sum_{i = 1}^n x_i\ ,
$$ and $\sigma^2$ by the  \emx {sample variance}\index{sample variance}
$$ s^2 = \frac 1n \sum_{i = 1}^n (x_i - \bar x)^2\ .
$$ Then $s$ is the  \emx {sample standard deviation.}\index{sample standard deviation}  These formulas
should remind the reader of the definitions of the theoretical mean and variance.  (Many
statisticians define the sample variance with the coefficient $1/n$ replaced by
$1/(n-1)$.  If this alternative definition is used, the expected value of $s^2$ is equal to $\sigma^2$. See
Exercise~\ref{exer 6.2.19}, part (d).)

Write a computer program that will roll a die $n$ times and compute the sample mean
and sample variance.  Repeat this experiment several times for~$n = 10$ and~$n =
1000$.  How well do the sample mean and sample variance estimate the true mean 7/2
and variance 35/12?

\i\label{exer 6.2.19} Show that, for the sample mean $\bar x$ and sample
variance $s^2$ as defined in Exercise~\ref{exer 6.2.18},
\begin{enumerate}
\item $E(\bar x) = \mu$.

\item $E\bigl((\bar x - \mu)^2\bigr) = \sigma^2/n$.

\item $E(s^2) = \frac {n-1}n\sigma^2$. \emx {Hint}: For (c) write
\begin{eqnarray*}
\sum_{i = 1}^n (x_i - \bar x)^2 & = & \sum_{i = 1}^n \bigl((x_i - \mu) -
(\bar x - \mu)\bigr)^2 \\
     & = & \sum_{i = 1}^n (x_i - \mu)^2 - 2(\bar x - \mu) \sum_{i = 1}^n (x_i -
\mu) + n(\bar x - \mu)^2 \\
     & = & \sum_{i = 1}^n (x_i - \mu)^2 - n(\bar x - \mu)^2,
\end{eqnarray*} and take expectations of both sides, using part (b) when necessary.

\item Show that if, in the definition of $s^2$ in Exercise~\ref{exer 6.2.18}, we
replace the coefficient $1/n$ by the coefficient $1/(n-1)$, then $E(s^2) = \sigma^2$.
(This shows why many statisticians use the coefficient $1/(n-1)$.  The number $s^2$
is used to estimate the unknown quantity $\sigma^2$.  If an estimator has an average
value which equals the quantity being estimated, then the estimator is said to be
 \emx {unbiased}.\index{unbiased estimator}  Thus, the statement $E(s^2) = \sigma^2$ says that $s^2$
is an unbiased estimator of $\sigma^2$.)
\end{enumerate}

\i\label{exer 6.2.20} Let $X$ be a random variable taking on values $a_1$,~$a_2$,
\dots, $a_r$ with probabilities $p_1$,~$p_2$, \dots,~$p_r$ and with $E(X) = \mu$.
Define the  \emx {spread}\index{spread} of $X$ as follows:
$$
\bar\sigma = \sum_{i = 1}^r |a_i - \mu|p_i\ .
$$ This, like the standard deviation, is a way to quantify the amount that a random
variable is spread out around its mean.  Recall that the variance of a sum of
mutually independent random variables is the sum of the individual variances.  The
square of the spread corresponds to the variance in a manner similar to the
correspondence between the spread and the standard deviation.  Show by an example
that it is not necessarily true that the square of the spread of the sum of two
independent random variables is the sum of the squares of the individual spreads.

\i\label{exer 6.2.21} We have two instruments that measure the distance between
two points.  The measurements given by the two instruments are random variables $X_1$
and
$X_2$ that are independent with $E(X_1) = E(X_2) = \mu$, where $\mu$ is the true
distance.  From experience with these instruments, we know the values of the
variances $\sigma_1^2$ and $\sigma_2^2$.  These variances are not necessarily the
same.  From two measurements, we estimate $\mu$ by the weighted average $\bar \mu
= wX_1 + (1 - w)X_2$.  Here $w$ is chosen in $[0,1]$ to minimize the variance of
$\bar \mu$.
\begin{enumerate}
\item What is $E(\bar \mu)$?

\item How should $w$ be chosen in $[0,1]$ to minimize the variance of
$\bar \mu$?
\end{enumerate}

\i\label{exer 6.2.22} Let $X$ be a random variable with $E(X) = \mu$ and $V(X) =
\sigma^2$.  Show that the function $f(x)$ defined by
$$ f(x) = \sum_\omega (X(\omega) - x)^2 p(\omega)
$$ has its minimum value when $x = \mu$.

\i\label{exer 6.2.23} Let $X$ and $Y$ be two random variables defined on the
finite sample space $\Omega$.  Assume that $X$,~$Y$, $X + Y$, and~$X - Y$ all have
the same distribution.  Prove that $P(X = Y = 0) = 1$.

\i\label{exer 6.2.24} If $X$ and $Y$ are any two random variables, then the {\em
covariance} of $X$ and $Y$ is defined by {\rm Cov}$(X,Y) = E((X - E(X))(Y -
E(Y)))$.  Note that {\rm Cov}$(X,X) = V(X)$.  Show that, if $X$ and
$Y$ are independent, then  {\rm Cov}$(X,Y) = 0$; and show, by an example, that we can
have {\rm Cov}$(X,Y) = 0$ and $X$ and $Y$ not independent.

\istar\label{exer 6.2.25} A professor wishes to make up a true-false exam\index{true-false exam}
with
$n$ questions.  She assumes that she can design the problems in such a way that a student
will answer the $j$th problem correctly with probability $p_j$, and that the answers
to the various problems may be considered independent experiments.  Let $S_n$ be the
number of problems that a student will get correct.  The professor wishes to choose
$p_j$ so that $E(S_n) = .7n$ and so that the variance of $S_n$ is as large as
possible.  Show that, to achieve this, she should choose $p_j = .7$ for all~$j$; that
is, she should make all the problems have the same difficulty.

\i\label{exer 6.2.26} (Lamperti\index{LAMPERTI, J.}\footnote{Private communication.}) An
urn contains exactly 5000 balls, of which an unknown number $X$ are white and the rest red, where $X$
is a random variable with a probability distribution on the integers 0,~1,~2, \dots,~5000.
\begin{enumerate}
\item Suppose we know that $E(X) = \mu$.  Show that this is enough to allow us to
calculate the probability that a ball drawn at random from the urn will be white.
What is this probability?

\item We draw a ball from the urn, examine its color, replace it, and then draw
another.  Under what conditions, if any, are the results of the two drawings
independent; that is, does
$$
 P({{\rm white},{\rm white}}) =  P({{\rm white}})^2\ ?
$$

\item Suppose the variance of $X$ is $\sigma^2$.  What is the probability of drawing
two white balls in part (b)?

\end{enumerate}

\i\label{exer 6.2.27} For a sequence of Bernoulli trials, let $X_1$ be the number
of trials until the first success.  For~$j \geq 2$, let $X_j$ be the number of trials
after the $(j - 1)$st success until the $j$th success.  It can be shown that
$X_1$,~$X_2$,~\dots is an independent trials process.
\begin{enumerate}
\item What is the common distribution, expected value, and variance for $X_j$?

\item Let $T_n = X_1 + X_2 +\cdots+ X_n$.  Then $T_n$ is the time until the
$n$th success.  Find $E(T_n)$ and $V(T_n)$.

\item Use the results of (b) to find the expected value and variance for the number
of tosses of a coin until the $n$th occurrence of a head.
\end{enumerate}

\i\label{exer 6.2.28} Referring to Exercise~\ref{sec 6.1}.\ref{exer 6.1.30}, find the
variance for the number of boxes of Wheaties bought before getting half of the players'
pictures and the variance for the number of additional boxes needed to get the second
half of the players' pictures.

\i\label{exer 6.2.99} In Example~\ref{exam 5.3}, assume that the book in question has
1000 pages.  Let
$X$ be the number of pages with no mistakes.  Show that $E(X) = 905$ and $V(X) = 86$.
Using these results, show that the probability is ${}
\leq .05$ that there will be more than 924 pages without errors or fewer than 866
pages without errors.

\i\label{exer 6.2.100} Let $X$ be Poisson distributed with parameter $\lambda$.
Show that $V(X) = \lambda$.

\end{LJSItem}

\choice{}{\section{Continuous Random Variables}\label{sec 6.3}

In this section we consider the properties of the expected value and the variance of
a continuous random variable.  These quantities are defined just as for discrete
random variables and share the same properties.

\subsection*{Expected Value}

\begin{definition}\label{def 6.5}  Let $X$ be a real-valued random variable with
density function $f(x)$.  The  \emx {expected value}\index{expected value} $\mu = E(X)$ is
defined by
$$
\mu = E(X) = \int_{-\infty}^{+\infty} xf(x)\,dx\ ,
$$
provided the integral
$$
\int_{-\infty}^{+\infty} |x|f(x)\,dx
$$
is finite.
\end{definition}

The reader should compare this definition with the corresponding one for discrete
random variables in Section~\ref{sec 6.1}.  Intuitively, we can interpret $E(X)$, as
we did in the previous sections, as the value that we should expect to obtain if we
perform a large number of independent experiments and average the resulting values of
$X$.

\par We can summarize the properties of $E(X)$ as follows (cf. Theorem~\ref{thm 6.1}).

\begin{theorem}\label{thm 6.10} If $X$ and $Y$ are real-valued random variables and
$c$ is any constant, then
\begin{eqnarray*}
E(X + Y) &=& E(X) + E(Y)\ , \\
E(cX)    &=& cE(X)\ .
\end{eqnarray*}
%\end{theorem}

The proof is very similar to the proof of Theorem~\ref{thm 6.1}, and we omit it.
\end{theorem}
\par More generally, if $X_1$,~$X_2$, \dots,~$X_n$ are $n$ real-valued random
variables, and
$c_1$,~$c_2$, \dots,~$c_n$ are $n$ constants, then
$$ E(c_1X_1 + c_2X_2 +\cdots+ c_nX_n) = c_1E(X_1) + c_2E(X_2) +\cdots+ c_nE(X_n)\ .
$$

\begin{example}\label{exam 6.16} Let $X$ be uniformly distributed on the interval
$[0, 1]$.  Then
$$E(X) = \int_0^1 x\,dx = 1/2\ .$$    It follows that if we choose a large number $N$
of random numbers from $[0,1]$ and take the average, then we can expect that this
average should be close to the expected value of 1/2.
\end{example}

\begin{example}\label{exam 6.17} Let $Z = (x, y)$ denote a point chosen uniformly and
randomly from the unit disk, as in the dart game in Example~\ref{exam 2.2.2} and let
$X = (x^2 + y^2)^{1/2}$ be the distance from $Z$ to the center of the disk.  The
density function of $X$ can easily be shown to equal $f(x) = 2x$, so by the
definition of expected value,
\begin{eqnarray*} E(X) & = & \int_0^1 x f(x)\,dx \\ & = & \int_0^1 x (2x)\,dx \\ & =
& \frac 23\ .
\end{eqnarray*}
\end{example}

\begin{example}\label{exam 6.18}   In the example of the couple meeting at the Inn
(Example~\ref{exam 2.2.7.4}), each person arrives at a time which is uniformly
distributed between 5:00 and 6:00 PM.  The random variable $Z$ under consideration is
the length of time the first person has to wait until the second one arrives.  It was
shown that
$$f_Z(z) = 2(1-z)\ ,$$ for $0 \le z \le 1$. Hence,
\begin{eqnarray*} E(Z) & = & \int_0^1 zf_Z(z)\,dz \\
     & = & \int_0^1 2z(1-z)\,dz \\
     & = & \Bigl[z^2 - \frac 23 z^3\Bigl]_0^1 \\  = \frac 13\ .
\end{eqnarray*}
\end{example}

\subsection*{Expectation of a Function of a Random Variable}

Suppose that $X$ is a real-valued random variable  and $\phi(x)$ is a continuous
function from {\bf R} to {\bf R}.  The following theorem is the continuous analogue
of Theorem~\ref{thm 6.3.5}.

\begin{theorem}\label{thm 6.11} If $X$ is a real-valued random variable and if
%$\phi : {\bf\rm R} \to {\bf\rm R}$ is a continuous real-valued function with domain
$\phi :$ {\bf R} $\to\ $  {\bf R}  is a continuous real-valued function with domain
$[a,b]$, then
$$ E(\phi(X)) = \int_{-\infty}^{+\infty} \phi(x) f_X(x)\, dx\ ,
$$ provided the integral exists.
\end{theorem}

For a proof of this theorem, see Ross.\index{ROSS, S.}\footnote{S. Ross,  \emx {A First Course in
Probability,} (New York: Macmillan, 1984), pgs. 241-245.}

\subsection*{Expectation of the Product of Two Random Variables}

In general, it is not true that $E(XY) = E(X)E(Y)$, since the integral of a product
is not the product of integrals.  But if $X$ and $Y$ are independent, then the
expectations multiply.

\begin{theorem}\label{thm 6.12} Let $X$ and $Y$ be independent real-valued continuous
random variables with finite expected values.  Then we have
$$ E(XY) = E(X)E(Y)\ .
$$
\proof We will prove this only in the case that the ranges of $X$ and $Y$ are contained in
the intervals $[a, b]$ and $[c, d]$, respectively.  Let the density functions of $X$ and $Y$ be
denoted by $f_X(x)$ and $f_Y(y)$, respectively.  Since $X$ and $Y$ are independent, the joint
density function of $X$ and $Y$ is the product of the individual density functions.  Hence
\begin{eqnarray*} E(XY) & = & \int_a^b \int_c^d xy f_X(x) f_Y(y)\, dy\,dx \\
      & = & \int_a^b x f_X(x)\, dx \int_c^d y f_Y(y)\, dy \\
      & = & E(X)E(Y)\ .
\end{eqnarray*}
\par
The proof in the general case involves using sequences of bounded random variables that approach $X$
and $Y$, and is somewhat technical, so we will omit it.
\end{theorem}

In the same way, one can show that if $X_1$,~$X_2$, \dots,~$X_n$ are $n$ mutually
independent real-valued random variables, then
$$
E(X_1 X_2 \cdots X_n) = E(X_1)\,E(X_2)\,\cdots\,E(X_n)\ .
$$

\begin{example}\label{exam 6.19} Let $Z = (X, Y)$ be a point chosen at random in the
unit square.  Let $A = X^2$ and
$B = Y^2$.  Then Theorem~\ref{thm 4.3} implies that $A$ and $B$ are independent.
Using Theorem~\ref{thm 6.11}, the expectations of $A$ and $B$ are easy to calculate:
\begin{eqnarray*} E(A) = E(B) & = & \int_0^1 x^2\,dx \\ & = & \frac 13\ .
\end{eqnarray*} Using Theorem~\ref{thm 6.12}, the expectation of $AB$ is just the
product of $E(A)$ and
$E(B)$, or 1/9.  The usefulness of this theorem is demonstrated by noting that  it is
quite a bit more difficult to calculate $E(AB)$ from the definition of expectation.
One finds that the density function of $AB$ is
$$f_{AB}(t) = \frac {-\log(t)}{4\sqrt t}\ ,$$ so
\begin{eqnarray*} E(AB) & = & \int_0^1 t f_{AB}(t)\,dt \\ & = & \frac 19\ .
\end{eqnarray*}
\end{example}

\begin{example}\label{exam 6.20} Again let $Z = (X, Y)$ be a point chosen at random
in the unit square, and let $W = X + Y$.    Then $Y$ and $W$ are not independent, and
we have
\begin{eqnarray*} E(Y) & = &\frac 12\ , \\ E(W) & = & 1\ , \\ E(YW) & = & E(XY + Y^2)
= E(X)E(Y) + \frac 13 = \frac 7{12}
\ne E(Y)E(W)\ .
\end{eqnarray*}
\end{example}

We turn now to the variance.

\subsection*{Variance}

\begin{definition}\label{def 6.6}  Let $X$ be a real-valued random variable with
density function $f(x)$.  The  \emx {variance}\index{variance} $\sigma^2 = V(X)$ is defined by
$$
\sigma^2 = V(X) = E((X - \mu)^2)\ .
$$
\end{definition}
The next result follows easily from Theorem~\ref{thm 6.3.5}.  There is another
way to calculate the variance of a continuous random variable, which is usually
slightly easier.  It is given in Theorem~\ref{thm 6.14}.
\begin{theorem}\label{thm 6.13.5} If $X$ is a real-valued random variable with $E(X)
= \mu$, then
$$
\sigma^2 = \int_{-\infty}^\infty (x - \mu)^2 f(x)\, dx\ .
$$
\end{theorem}
\par The properties listed in the next three theorems are all proved in exactly the
same way that the corresponding theorems for discrete random variables were proved in
Section~\ref{sec 6.2}.
\begin{theorem}\label{thm 6.13} If $X$ is a real-valued random variable defined on
$\Omega$ and $c$ is any constant, then (cf. Theorem~\ref{thm 6.6})
\begin{eqnarray*}
V(cX)    &=& c^2 V(X)\ , \\
V(X + c) &=& V(X)\ .
\end{eqnarray*}
\end{theorem}

\begin{theorem}\label{thm 6.14} If $X$ is a real-valued random variable with $E(X) =
\mu$, then (cf. Theorem~\ref{thm 6.7})
$$ V(X) = E(X^2) - \mu^2\ .
$$
\end{theorem}

\begin{theorem}\label{thm 6.15} If $X$ and $Y$ are independent real-valued random
variables on $\Omega$, then (cf. Theorem~\ref{thm 6.8})
$$ V(X + Y) = V(X) + V(Y)\ .
$$
\end{theorem}

\begin{example} (continuation of Example~\ref{exam 6.16})\label{exam 6.18.5} If
$X$ is uniformly distributed on $[0, 1]$, then, using Theorem~\ref{thm 6.14}, we have
$$  V(X) = \int_0^1 \Bigl(x - \frac 12 \Bigr)^2\, dx = \frac 1{12}\ .
$$
\end{example}

\begin{example}\label{exam 6.21} Let $X$ be an exponentially distributed random
variable with parameter $\lambda$.  Then the density function of $X$ is
$$ f_X(x) = \lambda e^{-\lambda x}\ .
$$ From the definition of expectation and integration by parts, we have
\begin{eqnarray*} E(X) & = & \int_0^\infty x f_X(x)\, dx \\
     & = & \lambda \int_0^\infty x e^{-\lambda x}\, dx \\
     & = & \biggl.-xe^{-\lambda x}\biggr|_0^\infty + \int_0^\infty e^{-\lambda x}\,
dx \\
     & = & 0 + \biggl.\frac {e^{-\lambda x}}{-\lambda}\biggr|_0^\infty =
\frac 1\lambda\ .
\end{eqnarray*}

Similarly, using Theorems~\ref{thm 6.11} and \ref{thm 6.14}, we have
\begin{eqnarray*} V(X) & = & \int_0^\infty x^2 f_X(x)\, dx - \frac 1{\lambda^2} \\
     & = & \lambda \int_0^\infty x^2 e^{-\lambda x}\, dx - \frac 1{\lambda^2} \\
     & = & \biggl.-x^2 e^{-\lambda x}\biggr|_0^\infty + 2\int_0^\infty x e^{-\lambda
x}\, dx - \frac 1{\lambda^2} \\
     & = & \biggl.-x^2 e^{-\lambda x}\biggr|_0^\infty - \biggl.\frac {2xe^{-\lambda
x}}\lambda\biggr|_0^\infty - \biggl.\frac 2{\lambda^2} e^{-\lambda x}\biggr|_0^\infty
-
\frac 1{\lambda^2} =
\frac 2{\lambda^2} - \frac 1{\lambda^2} = \frac 1{\lambda^2}\ .
\end{eqnarray*} In this case, both $E(X)$ and $V(X)$ are finite if $\lambda > 0$.
\end{example}

\begin{example}\label{exam 6.22} Let $Z$ be a standard normal random variable with
density function
$$ f_Z(x) = \frac 1{\sqrt{2\pi}} e^{-x^2/2}\ .
$$  Since this density function is symmetric with respect to the $y$-axis, then it is
easy to show that
$$\int_{-\infty}^\infty x f_Z(x)\, dx$$ has value 0.  The reader should recall
however, that the expectation is defined to be the above integral only if the integral
$$\int_{-\infty}^\infty |x| f_Z(x)\, dx$$ is finite.  This integral equals
$$2\int_0^\infty x f_Z(x)\, dx\ ,$$ which one can easily show is finite.  Thus, the
expected value of $Z$ is 0.
\par To calculate the variance of $Z$, we begin by applying Theorem~\ref{thm 6.14}:
$$V(Z) =  \int_{-\infty}^{+\infty} x^2 f_Z(x)\, dx - \mu^2\ .$$ If we write $x^2$ as
$x\cdot x$, and integrate by parts, we obtain
$$\biggl.\frac 1{\sqrt{2\pi}} (-x e^{-x^2/2})\biggr|_{-\infty}^{+\infty} + \frac
1{\sqrt{2\pi}} \int_{-\infty}^{+\infty} e^{-x^2/2}\, dx\ .
$$ The first summand above can be shown to equal 0, since as $x \rightarrow \pm
\infty$, $e^{-x^2/2}$ gets small more quickly than $x$ gets large.  The second
summand is just the standard normal density integrated over its domain, so the value
of this summand is 1.  Therefore, the variance of the standard normal density equals
1.
\par Now let $X$ be a (not necessarily standard) normal random variable with
parameters
$\mu$ and $\sigma$.  Then the density function of $X$ is
$$ f_X(x) = \frac 1{\sqrt{2\pi}\sigma} e^{-(x - \mu)^2/2\sigma^2}\ .
$$  We can write $X = \sigma Z + \mu$, where $Z$ is a standard normal random
variable.  Since
$E(Z) = 0$ and $V(Z) = 1$ by the calculation above, Theorems~\ref{thm 6.10} and
\ref{thm 6.13} imply that
\begin{eqnarray*}
E(X)  &=& E(\sigma Z + \mu)\ =\ \mu\ , \\
V(X)  &=& V(\sigma Z + \mu)\ =\ \sigma^2\ .
\end{eqnarray*}
\end{example}

\begin{example}\label{exam 6.23} Let $X$ be a continuous random variable with the
Cauchy density function
$$ f_X(x) = \frac {a}{\pi} \frac {1}{a^2 + x^2}\ .
$$ Then the expectation of $X$ does not exist, because the integral
$$
\frac a\pi \int_{-\infty}^{+\infty} \frac {|x|\,dx}{a^2 + x^2}
$$ diverges.  Thus the variance of $X$ also fails to exist.  Densities whose variance
is not defined, like the Cauchy density, behave quite differently in a number of
important respects from those whose variance is finite.  We shall see one instance of
this difference in Section~\ref{sec 8.2}.
\end{example}

\subsection*{Independent Trials}

\begin{corollary}\label{cor 6.1}
If $X_1$,~$X_2$, \dots,~$X_n$ is an independent trials
process of real-valued random variables, with $E(X_i) = \mu$ and $V(X_i) = \sigma^2$,
and if
\begin{eqnarray*} S_n & = & X_1 + X_2 +\cdots+ X_n\ , \\ A_n & = & \frac {S_n}n\ ,
\end{eqnarray*}
then
\begin{eqnarray*}
E(S_n) & = & n\mu\ ,\\
E(A_n) & = & \mu\ ,\\
V(S_n) & = & n\sigma^2\ ,\\
V(A_n) & = & \frac {\sigma^2} n\ .
\end{eqnarray*}
It follows that if we set
$$
S_n^* = \frac {S_n - n\mu}{\sqrt{n\sigma^2}}\ ,
$$
then
\begin{eqnarray*}
E(S_n^*) & = & 0\ ,\\
V(S_n^*) & = & 1\ .
\end{eqnarray*}
We say that $S_n^*$ is a  \emx {standardized version of} $S_n$ (see
Exercise~\ref{exer 6.2.13} in Section~\ref{sec 6.2}).
\end{corollary}

\subsection*{Queues}\index{queues}

\begin{example}\label{exam 6.24} Let us consider again the queueing problem, that is,
the problem of the customers waiting in a queue for service (see Example~\ref{exam
5.21}).  We suppose again that customers join the queue in such a way that the time
between arrivals is an exponentially distributed random variable $X$ with density
function
$$ f_X(t) = \lambda e^{-\lambda t}\ .
$$ Then the expected value of the time between arrivals is simply $1/\lambda$ (see
Example~\ref{exam 6.21}), as was stated in Example~\ref{exam 5.21}.  The reciprocal
$\lambda$ of this expected value is often referred to as the  \emx {arrival rate.}  The
\emx {service time} of an individual who is first in line is defined to be the amount
of time that the person stays at the head of the line before leaving.  We suppose that
the customers are served in such a way that the service time is another exponentially
distributed random variable
$Y$ with density function
$$ f_X(t) = \mu e^{-\mu t}\ .
$$ Then the expected value of the service time is
$$ E(X) = \int_0^\infty t f_X(t)\, dt = \frac 1\mu\ .
$$ The reciprocal $\mu$ if this expected value is often referred to as the {\em
service rate.}
\par We expect on grounds of our everyday experience with queues that if the service
rate is greater than the arrival rate, then the average queue size will tend to
stabilize, but if the service rate is less than the arrival rate, then the queue will
tend to increase in length without limit (see Figure~\ref{fig 5.17}).  The simulations
in Example~\ref{exam 5.21} tend to bear out our everyday experience.  We can make this
conclusion more precise if we introduce the  \emx {traffic intensity} as the product
$$
\rho = ({\rm arrival\ rate})({\rm average\ service\ time}) = \frac \lambda\mu =
\frac {1/\mu}{1/\lambda}\ .
$$ The traffic intensity is also the ratio of the average service time to the average
time between arrivals.  If the traffic intensity is less than~1 the queue will
perform reasonably, but if it is greater than~1 the queue will grow indefinitely
large.  In the critical case of $\rho = 1$, it can be shown that the queue will
become large but there will always be times at which the queue is empty.\footnote{L.
Kleinrock,  \emx {Queueing Systems,} vol.~2 (New York: John Wiley and Sons, 1975).}

In the case that the traffic intensity is less than~1 we can consider the length of
the queue as a random variable $Z$ whose expected value is finite,
$$ E(Z) = N\ .
$$

The time spent in the queue by a single customer can be considered as a random
variable $W$ whose expected value is finite,
$$ E(W) = T\ .
$$ Then we can argue that, when a customer joins the queue, he expects to find $N$
people ahead of him, and when he leaves the queue, he expects to find $\lambda T$
people behind him.  Since, in equilibrium, these should be the same, we would expect
to find that
$$ N = \lambda T\ .
$$ This last relationship is called  \emx {Little's law for queues.}\index{Little's
law for queues}\footnote{ibid., p.~17.}  We will not prove it here.  A proof may be found in
Ross.\index{ROSS, S.}\footnote{S. M. Ross,  \emx {Applied Probability Models with Optimization
Applications,} (San Francisco: Holden-Day, 1970)}  Note that in this case we are counting the
waiting time of all customers, even those that do not have to wait at all.  In our simulation in
Section~\ref{sec 4.2}, we did not consider these customers.
\par If we knew the expected queue length then we could use Little's law to obtain
the expected waiting time, since
$$ T = \frac N\lambda\ .
$$ The queue length is a random variable with a discrete distribution.  We can estimate
this distribution by simulation, keeping track of the queue lengths at the times at
which a customer arrives.  We show the result of this simulation (using the program {\bf
Queue}) in Figure~\ref{fig 6.5}.
\putfig{4truein}{PSfig6-5}{Distribution of queue lengths.}{fig 6.5}
\par We note that the distribution appears to be a geometric distribution.  In the study
of queueing theory it is shown that the distribution for the queue length in equilibrium
is indeed a geometric distribution with
$$ s_j = (1 - \rho) \rho^j \qquad {\rm for}\  j = 0, 1, 2, \dots\ ,
$$ if $\rho < 1$.
The expected value of a random variable with this distribution is
$$ N = \frac \rho{(1 - \rho)}
$$ (see Example~\ref{exam 6.8}).  Thus by Little's result the expected waiting time is
$$ T = \frac \rho{\lambda(1 - \rho)} = \frac 1{\mu - \lambda}\ ,
$$ where $\mu$ is the service rate, $\lambda$ the arrival rate, and $\rho$ the
traffic intensity.
\par
In our simulation, the arrival rate is 1 and the service rate is 1.1.  Thus, the
traffic intensity is $1/1.1 = 10/11$, the expected queue size is
$$
\frac {10/11}{(1 - 10/11)} = 10\ ,
$$ and the expected waiting time is
$$
\frac 1{1.1 - 1} = 10\ .
%*********The simulation values weren't very close to the theoretical ones.
%Do we want to rerun it?
$$ In our simulation the average queue size was 8.19 and the average waiting time
was 7.37.   In Figure~\ref{fig 6.5.5}, we show the histogram for the waiting times.
This histogram suggests that the density for the waiting times is exponential with
parameter
$\mu - \lambda$, and this is the case.
\putfig{4truein}{PSfig6-5-5}{Distribution of queue waiting times.}{fig 6.5.5}
\end{example}

\exercises
\begin{LJSItem}

\i\label{exer 6.3.1} Let $X$ be a random variable with range $[-1,1]$ and let
$f_X(x)$ be the density function of $X$.  Find $\mu(X)$ and $\sigma^2(X)$ if, for $|x|
< 1$,

\begin{enumerate}
\item $f_X(x) = 1/2$.

\item $f_X(x) = |x|$.

\item $f_X(x) = 1 - |x|$.

\item $f_X(x) = (3/2) x^2$.
\end{enumerate}

\i\label{exer 6.3.3} Let $X$ be a random variable with range $[-1,1]$ and
$f_X$ its density function.  Find $\mu(X)$ and $\sigma^2(X)$ if, for $|x| > 1$,
$f_X(x) = 0$, and for $|x| < 1$,

\begin{enumerate}
\item $f_X(x) = (3/4)(1 - x^2)$.

\item $f_X(x) = (\pi/4)\cos(\pi x/2)$.

\item $f_X(x) = (x + 1)/2$.

\item $f_X(x) = (3/8)(x + 1)^2$.
\end{enumerate}

\i\label{exer 6.3.5} The lifetime, measure in hours, of the ACME super light bulb
is a random variable $T$ with density function $f_T(t) = \lambda^2 t e^{-\lambda t}$,
where
$\lambda = .05$.  What is the expected lifetime of this light bulb?  What is its
variance?

\i\label{exer 6.3.6} Let $X$ be a random variable with range $[-1,1]$ and density
function $f_X(x) = ax + b$ if $|x| < 1$.

\begin{enumerate}
\item Show that if $\int_{-1}^{+1} f_X(x)\, dx = 1$, then $b = 1/2$.

\item Show that if $f_X(x) \geq 0$, then $-1/2 \leq a \leq 1/2$.

\item Show that $\mu = (2/3)a$, and hence that $-1/3 \leq \mu \leq 1/3$.

\item Show that $\sigma^2(X) = (2/3)b - (4/9)a^2 = 1/3 - (4/9)a^2$.
\end{enumerate}

\i\label{exer 6.3.7} Let $X$ be a random variable with range $[-1,1]$ and density
function
$f_X(x) = ax^2 + bx + c$ if $|x| < 1$ and~0 otherwise.

\begin{enumerate}
\item Show that $2a/3 + 2c = 1$ (see Exercise~\ref{exer 6.3.6}).

\item Show that $2b/3 = \mu(X)$.

\item Show that $2a/5 + 2c/3 = \sigma^2(X)$.

\item Find $a$,~$b$, and~$c$ if $\mu(X) = 0$, $\sigma^2(X) = 1/15$, and sketch the
graph of $f_X$.

\item Find $a$,~$b$, and~$c$ if $\mu(X) = 0$, $\sigma^2(X) = 1/2$, and sketch the
graph of $f_X$.
\end{enumerate}

\i\label{exer 6.3.8} Let $T$ be a random variable with range $[0,\infty]$ and
$f_T$ its density function.  Find $\mu(T)$ and $\sigma^2(T)$ if, for $t < 0$, $f_T(t)
= 0$, and for $t > 0$,
\begin{enumerate}

\item $f_T(t) = 3e^{-3t}$.

\item $f_T(t) = 9te^{-3t}$.

\item $f_T(t) = 3/(1 + t)^4$.
\end{enumerate}

\i\label{exer 6.3.9} Let $X$ be a random variable with density function $f_X$.
Show, using elementary calculus, that the function
$$
\phi(a) = E((X - a)^2)
$$ takes its minimum value when $a = \mu(X)$, and in that case $\phi(a) =
\sigma^2(X)$.

\i\label{exer 6.3.10} Let $X$ be a random variable with mean $\mu$ and variance
$\sigma^2$.  Let $Y = aX^2 + bX + c$.  Find the expected value of $Y$.

\i\label{exer 6.3.11} Let $X$,~$Y$, and~$Z$ be independent random variables, each
with mean
$\mu$ and variance $\sigma^2$.

\begin{enumerate}
\item Find the expected value and variance of $S = X + Y + Z$.

\item Find the expected value and variance of $A = (1/3)(X + Y + Z)$.

\item Find the expected value of $S^2$ and $A^2$.
\end{enumerate}

\i\label{exer 6.3.12} Let $X$ and $Y$ be independent random variables with uniform
density functions on $[0,1]$.  Find

\begin{enumerate}
\item $E(|X - Y|)$.

\item $E(\max(X,Y))$.

\item $E(\min(X,Y))$.

\item $E(X^2 + Y^2)$.

\item $E((X + Y)^2)$.
\end{enumerate}

\i\label{exer 6.3.13} The Pilsdorff\index{Pilsdorff Beer Company} Beer Company runs a fleet of
trucks along the 100~mile road from Hangtown\index{Hangtown} to Dry Gulch\index{Dry Gulch}.  The
trucks are old, and are apt to break down at any point along the road with equal probability.  Where
should the company locate a garage so as to minimize the expected distance from a typical breakdown
to the garage?  In other words, if $X$ is a random variable giving the location of the
breakdown, measured, say, from Hangtown, and $b$ gives the location of the garage,
what choice of $b$ minimizes $E(|X - b|)$?  Now suppose
$X$ is not distributed uniformly over $[0,100]$, but instead has density function
$f_X(x) = 2x/10{,}000$.  Then what choice of $b$ minimizes $E(|X - b|)$?

\i\label{exer 6.3.14} Find $E(X^Y)$, where $X$ and $Y$ are independent random
variables which are uniform on $[0, 1]$.  Then verify your answer by simulation.

\i\label{exer 6.3.15} Let $X$ be a random variable that takes on nonnegative
values and has distribution function $F(x)$.  Show that
$$ E(X) = \int_0^\infty (1 - F(x))\, dx\ .
$$
 \emx {Hint}: Integrate by parts.

Illustrate this result by calculating $E(X)$ by this method if $X$ has an exponential
distribution $F(x) = 1 - e^{-\lambda x}$ for $x \geq 0$, and $F(x) = 0$ otherwise.

\i\label{exer 6.3.15.5} Let $X$ be a continuous random variable with density
function $f_X(x)$.  Show that if
$$
\int_{-\infty}^{+\infty} x^2 f_X(x)\, dx < \infty\ ,
$$ then
$$
\int_{-\infty}^{+\infty} |x| f_X(x)\, dx < \infty\ .
$$
 \emx {Hint}:  Except on the interval $[-1, 1]$, the first integrand is greater than the
second integrand.

\i\label{exer 6.3.16} Let $X$ be a random variable distributed uniformly over
$[0,20]$.  Define a new random variable $Y$ by $Y = \lfloor X\rfloor$ (the greatest
integer in $X$).  Find the expected value of~$Y$.  Do the same for $Z =
\lfloor X + .5\rfloor$.  Compute $E\bigl(|X-Y|\bigr)$ and $E\bigl(|X-Z|\bigr)$.
(Note that
$Y$ is the value of~$X$ rounded off to the nearest smallest integer, while $Z$ is the
value of~$X$ rounded off to the nearest integer.  Which method of rounding off is
better?  Why?)

\i\label{exer 6.3.17} Assume that the lifetime of a diesel engine part is a random
variable $X$ with density $f_X$.  When the part wears out, it is replaced by another
with the same density.  Let $N(t)$ be the number of parts that are used in time
$t$.  We want to study the random variable $N(t)/t$.  Since parts are replaced on the
average every $E(X)$ time units, we expect about $t/E(X)$ parts to be used in time
$t$.  That is, we expect that
$$
\lim_{t \to \infty} E \Bigl(\frac {N(t)}t\Bigr) = \frac 1{E(X)}\ .
$$ This result is correct but quite difficult to prove.  Write a program that will
allow you to specify the density $f_X$, and the time $t$, and simulate this
experiment to find $N(t)/t$.  Have your program repeat the experiment 500 times and
plot a bar graph for the random outcomes of $N(t)/t$.  From this data, estimate
$E(N(t)/t)$ and compare this with $1/E(X)$.  In particular, do this for~$t = 100$
with the following two densities:

\begin{enumerate}
\item $f_X = e^{-t}$.

\item $f_X = te^{-t}$.
\end{enumerate}

\i\label{exer 6.3.18} Let $X$ and $Y$ be random variables.  The  \emx {covariance}
$\rm {Cov}(X,Y)$ is defined by (see Exercise~\ref{sec 6.2}.\ref{exer 6.2.24})
$$
\rm {cov}(X,Y) = E ((X - \mu(X))(Y - \mu(Y)) )\ .
$$
\begin{enumerate}
\item Show that $\rm {cov}(X,Y) = E(XY) - E(X)E(Y)$.

\item Using (a), show that ${\rm cov}(X,Y) = 0$, if
$X$ and $Y$ are independent.  (Caution: the converse is  \emx {not} always true.)

\item Show that $V(X + Y) = V(X) + V(Y) + 2{\rm cov}(X,Y)$.
\end{enumerate}

\i\label{exer 6.3.19} Let $X$ and $Y$ be random variables with positive variance.
The \emx {correlation} of $X$ and $Y$ is defined as
$$
\rho(X,Y) = \frac{{\rm cov}(X,Y)}{\sqrt{V(X)V(Y)}}\ .
$$
\begin{enumerate}
\item Using Exercise~\ref{exer 6.3.18}(c), show that
$$ 0 \leq V\left( \frac X{\sigma(X)} + \frac Y{\sigma(Y)} \right) = 2(1 +
\rho(X,Y))\ .
$$

\item Now show that
$$ 0 \leq V\left( \frac X{\sigma(X)} - \frac Y{\sigma(Y)} \right) = 2(1 -
\rho(X,Y))\ .
$$

\item Using (a) and (b), show that
$$ -1 \leq \rho(X,Y) \leq 1\ .
$$
\end{enumerate}

\i\label{exer 6.3.20} Let $X$ and $Y$ be independent random variables with uniform
densities in $[0,1]$.  Let $Z = X + Y$ and $W = X - Y$.  Find

\begin{enumerate}
\item $\rho(X,Y)$ (see Exercise~\ref{exer 6.3.19}).

\item $\rho(X,Z)$.

\item $\rho(Y,W)$.

\item $\rho(Z,W)$.
\end{enumerate}

\istar\label{exer 6.3.21} When studying certain physiological data, such as heights
of fathers and sons, it is often natural to assume that these data (e.g., the heights
of the fathers and the heights of the sons) are described by random variables with
normal densities.  These random variables, however, are not independent but rather
are correlated.  For example, a two-dimensional standard normal density for
correlated random variables has the form
$$ f_{X,Y}(x,y) = \frac 1{2\pi\sqrt{1 - \rho^2}} \cdot e^{-(x^2 - 2\rho xy + y^2)/2(1
- \rho^2)}\ .
$$
\begin{enumerate}
\item Show that $X$ and $Y$ each have standard normal densities.

\item Show that the correlation of $X$ and $Y$ (see Exercise~\ref{exer 6.3.19}) is
$\rho$.
\end{enumerate}

\istar\label{exer 6.3.22} For correlated random variables $X$ and $Y$ it is natural to
ask for the expected value for~$X$ given~$Y$.  For example, Galton\index{GALTON, F.} calculated the
expected value of the height of a son given the height of the father.  He used this
to show that tall men can be expected to have sons who are less tall on the average.
Similarly, students who do very well on one exam can be expected to do less well on
the next exam, and so forth.  This is called  \emx {regression on the mean.}\index{regression on
the mean}  To define this conditional expected value, we first define a conditional density of~$X$
given~$Y = y$ by
$$ f_{X|Y}(x|y) = \frac {f_{X,Y}(x,y)}{f_Y(y)}\ ,
$$ where $f_{X,Y}(x,y)$ is the joint density of $X$ and $Y$, and $f_Y$ is the density
for $Y$.  Then the conditional expected value of~$X$ given~$Y$ is
$$ E(X|Y = y) = \int_a^b x f_{X|Y}(x|y)\, dx\ .
$$ For the normal density in Exercise~\ref{exer 6.3.21}, show that the conditional
density of $f_{X|Y}(x|y)$ is normal with mean $\rho y$ and variance $1 - \rho^2$.
From this we see that if $X$ and $Y$ are positively correlated $(0 < \rho < 1)$,
and if $y > E(Y)$, then the expected value for~$X$ given~$Y = y$ will be less than~$y$
(i.e., we have regression on the mean).

\i\label{exer 6.3.23} A point $Y$ is chosen at random from $[0,1]$.  A second
point $X$ is then chosen from the interval $[0,Y]$.  Find the density for $X$.  \emx
{Hint}: Calculate $f_{X|Y}$ as in Exercise~\ref{exer 6.3.22} and then use
$$ f_X(x) = \int_x^1 f_{X|Y}(x|y) f_Y(y)\, dy\ .
$$ Can you also derive your result geometrically?

%**********Provide an answer in the answer key to the following problem  (it has been significantly
%changed).   In addition, we should find out what it means to be inside one of the ellipses
%discussed in the problem.

\istar\label{exer 6.3.24} Let $X$ and $V$ be two standard normal random variables.  Let $\rho$
be a real number between -1 and 1.
\begin{enumerate}
\item
Let $Y = \rho X + \sqrt{1 - \rho^2} V$.  Show that $E(Y) = 0$ and $Var(Y) = 1$.  We shall see later
(see Example~\ref{exam 7.8} and Example~\ref{exam 10.3.3}), that the sum of two independent normal
random variables is again normal.  Thus, assuming this fact, we have shown that $Y$ is standard
normal.
\item
Using Exercises~\ref{exer 6.3.18} and \ref{exer 6.3.19}, show that the correlation of $X$
and $Y$ is $\rho$.
\item
In Exercise~\ref{exer 6.3.21}, the joint density function $f_{X,Y}(x, y)$ for the random variable
$(X, Y)$ is given.  Now suppose that we want to know the set of points $(x, y)$ in the $xy$-plane
such that $f_{X,Y}(x, y) = C$ for some constant $C$.  This set of points is called a set of constant
density.  Roughly speaking, a set of constant density is a set of points where the outcomes $(X, Y)$
are equally likely to fall.  Show that for a given $C$, the set of points of constant density is
a curve whose equation is
$$x^2 - 2\rho x y + y^2 = D\ ,$$
where $D$ is a constant which depends upon $C$.  (This curve is an ellipse.)
\item
One can plot the ellipse in part (c) by using the parametric equations
\begin{eqnarray*} x & = & \frac {r\cos\theta}{\sqrt{2(1 - \rho)}} + \frac
{r\sin\theta}{\sqrt{2(1 +
\rho)}}\ , \\ y & = & \frac {r\cos\theta}{\sqrt{2(1 - \rho)}} - \frac
{r\sin\theta}{\sqrt{2(1 +
\rho)}}\ .
\end{eqnarray*}
Write a program to plot 1000 pairs $(X, Y)$ for $\rho = -1/2, 0, 1/2$.  For each plot,
have your program plot the above parametric curves for $r = 1, 2, 3$.
\end{enumerate}

\istar\label{exer 6.3.25} Following Galton, let us assume that the fathers and sons
have heights that are dependent normal random variables.  Assume that the average
height is 68~inches, standard deviation is 2.7~inches, and the correlation coefficient
is~.5 (see Exercises~\ref{exer 6.3.21}~and~\ref{exer 6.3.22}).  That is, assume that
the heights of the fathers and sons have the form $2.7X + 68$ and $2.7Y + 68$,
respectively, where $X$ and $Y$ are correlated standardized normal random variables,
with correlation coefficient~.5.

\begin{enumerate}
\item What is the expected height for the son of a father whose height is 72~inches?

\item Plot a scatter diagram of the heights of 1000 father and son pairs.  \emx
{Hint}: You can choose standardized pairs as in Exercise~\ref{exer 6.3.24} and then
plot $(2.7X + 68, 2.7Y + 68)$.
\end{enumerate}

%***********This problem is very time-consuming for the student to do.  Either we
%should make a file containing the data, or else we should delete the problem.  We
%will try to find the original data (instead of just the table).

\istar\label{exer 6.3.26} When we have pairs of data $(x_i,y_i)$ that are outcomes of
the pairs of dependent random variables $X$,~$Y$ we can estimate the coorelation
coefficient
$\rho$ by
$$
\bar r = \frac {\sum_i (x_i - \bar x)(y_i - \bar y)}{(n - 1)s_Xs_Y}\ ,
$$ where $\bar x$ and $\bar y$ are the sample means for $X$ and $Y$,
respectively, and $s_X$ and $s_Y$ are the sample standard deviations for $X$ and $Y$
(see Exercise~\ref{sec 6.2}.\ref{exer 6.2.18}).  Write a
program to compute the sample means, variances, and correlation for such dependent
data.  Use your program to compute these quantities for Galton's data on heights of
parents and children given in Appendix~B.
\par
Plot the equal density ellipses as defined in Exercise~\ref{exer 6.3.24} for~$r =
4$,~6, and~8, and on the same graph print the values that appear in the table at the
appropriate points.  For example, print 12 at the point
$(70.5,68.2)$, indicating that there were 12 cases where the parent's height was 70.5
and the child's was 68.12.  See if Galton's data is consistent with the equal density
ellipses.

\i\label{exer 6.3.27} (from Hamming\index{HAMMING, R. W.}\footnote{R.~W.~Hamming,  \emx {The Art
of  Probability for Scientists and Engineers} (Redwood City:  Addison-Wesley, 1991), p.~192.})
Suppose you are standing on the bank of a straight river.
\begin{enumerate}
\item Choose, at random, a direction which will keep you on dry land, and walk 1 km
in that direction.  Let $P$ denote your position.  What is the expected distance from
$P$ to the river?
\item Now suppose you proceed as in part (a), but when you get to $P$, you pick a random
direction (from among  \emx {all} directions) and walk 1 km.  What is the probability
that you will reach the river before the second walk is completed?
\end{enumerate}

\i\label{exer 6.3.28} (from Hamming\index{HAMMING, R. W.}\footnote{ibid., pg. 205.})  A game is
played as follows:  A random number $X$ is chosen uniformly from $[0, 1]$.  Then a sequence $Y_1,
Y_2,
\ldots$ of random numbers is chosen independently and uniformly from $[0, 1]$.  The
game ends the first time that $Y_i~>~X$.  You are then paid $(i-1)$ dollars.  What is
a fair entrance fee for this game?

\i\label{exer 6.3.29} A long needle of length $L$ much bigger than 1 is dropped on a
grid with horizontal and vertical lines one unit apart.  Show that the average number
$a$ of lines crossed is approximately
$$
a = \frac{4L}\pi\ .
$$

\end{LJSItem}}
%\end{document}