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\title{PS7_Gao}
\author{ningjing gao }
\date{March 2019}

\begin{document}

\maketitle

\section{Summary Table}
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccccccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Pctl(25)} & \multicolumn{1}{c}{Pctl(75)} & \multicolumn{1}{c}{Max} \\ 
\hline \\[-1.8ex] 
logwage & 1,669 & 1.625 & 0.386 & 0.005 & 1.362 & 1.936 & 2.261 \\ 
hgc & 2,229 & 13.101 & 2.524 & 0 & 12 & 15 & 18 \\ 
tenure & 2,229 & 5.971 & 5.507 & 0.000 & 1.583 & 9.333 & 25.917 \\ 
age & 2,229 & 39.152 & 3.062 & 34 & 36 & 42 & 46 \\ 
\hline \\[-1.8ex] 
\end{tabular} 
\end{table} 

Logwages are missing as 560 are missing out of 2229 observations in the table. the missing rate is 560/2229=0.2512. it should be MNAR. The variable is most likely to be MNAR.


\section{}
I prefer the Mice. the real β1 = 0.093. others are away from the 0.093 of the dependent variables. 

\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lccc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
\cline{2-4} 
\\[-1.8ex] & \multicolumn{3}{c}{logwage} \\ 
\\[-1.8ex] & (1) & (2) & (3)\\ 
\hline \\[-1.8ex] 
 hgc & 0.062$^{***}$ & 0.049$^{***}$ & 0.062$^{***}$ \\ 
  & (0.005) & (0.004) & (0.005) \\ 
  & & & \\ 
 collegenot college grad & 0.146$^{***}$ & 0.160$^{***}$ & 0.145$^{***}$ \\ 
  & (0.035) & (0.026) & (0.034) \\ 
  & & & \\ 
 tenure & 0.023$^{***}$ & 0.015$^{***}$ & 0.050$^{***}$ \\ 
  & (0.002) & (0.001) & (0.005) \\ 
  & & & \\ 
 I(tenure$\hat{\mkern6mu}$2) &  &  & $-$0.002$^{***}$ \\ 
  &  &  & (0.0003) \\ 
  & & & \\ 
 age & $-$0.001 & $-$0.001 & 0.0004 \\ 
  & (0.003) & (0.002) & (0.003) \\ 
  & & & \\ 
 marriedsingle & $-$0.024 & $-$0.029$^{**}$ & $-$0.022 \\ 
  & (0.018) & (0.014) & (0.018) \\ 
  & & & \\ 
 Constant & 0.639$^{***}$ & 0.833$^{***}$ & 0.534$^{***}$ \\ 
  & (0.146) & (0.115) & (0.146) \\ 
  & & & \\ 
\hline \\[-1.8ex] 
Observations & 1,669 & 2,229 & 1,669 \\ 
R$^{2}$ & 0.195 & 0.132 & 0.208 \\ 
Adjusted R$^{2}$ & 0.192 & 0.130 & 0.206 \\ 
Residual Std. Error & 0.346 (df = 1663) & 0.311 (df = 2223) & 0.344 (df = 1662) \\ 
F Statistic & 80.508$^{***}$ (df = 5; 1663) & 67.496$^{***}$ (df = 5; 2223) & 72.917$^{***}$ (df = 6; 1662) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table}  


\section{Question 8}
i need to tell you honestly, i have not get any useful alaysis. Even I do not want to disappoint you. i am doing coronavirus alaysis. but it is hard to get what i want like a graph. I can general growth rate for presumptive case. and compare the different state data.
i swear, i will focus on the project furing spring break. hope i can do better after that time.
 

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