query: Generate two variables with precise pre-specified correlation
positive_passages.docid: 102514
positive_passages.text: Generate a random variable with a defined correlation to an existing variable(s)
negative_passages.docid: 49160
negative_passages.text: Joint distribution of two dependent variables
negative_passages.docid: 39225
negative_passages.text: Generating values from a multivariate Gaussian distribution
negative_passages.docid: 61256
negative_passages.text: How to generate correlated random numbers (given means, variances and degree of correlation)?
negative_passages.docid: 26020
negative_passages.text: Nice expression for minimum of three variables?
negative_passages.docid: 34988
negative_passages.text: How to do indirect variable evaluation
negative_passages.docid: 11582
negative_passages.text: How to compare two Pearson correlation coefficients
negative_passages.docid: 50030
negative_passages.text: Does it make sense to interact 2 dummy variables?
negative_passages.docid: 10479
negative_passages.text: How to simulate data that satisfy specific constraints such as having specific mean and standard deviation?
negative_passages.docid: 47660
negative_passages.text: How to generate Bernoulli random variables with common correlation $\rho$?
negative_passages.docid: 151986
negative_passages.text: Generating a vector of dummy variables
negative_passages.docid: 9930
negative_passages.text: Correlations between continuous and categorical (nominal) variables
negative_passages.docid: 14956
negative_passages.text: Sum of 2 Normally Distributed Random Variables With a Correlation
negative_passages.docid: 124391
negative_passages.text: Correlation with a constant
negative_passages.docid: 16191
negative_passages.text: How to show operations on two random variables (each Bernoulli) are dependent but not correlated?
negative_passages.docid: 35219
negative_passages.text: How do I study the "correlation" between a continuous variable and a categorical variable?
negative_passages.docid: 17635
negative_passages.text: How to create an arbitrary covariance matrix