When you have two or more data variables, one question that you face is whether there is any co-movement between the variables. I start with correlation and covariance, two measures of co-movement, and look at similarities and differences. I then move on to a simple regression, where you use an independent variable (X) to explain a dependent variable (Y), and how to read the output from a regression (F statistics, R squared, t statistics and p values). I conclude the session by expanding the discussion to multiple regressions, and the requirements for a "good" regression in terms of correlation across independent variables as well as the behavior of residuals (prediction errors).
Slides: http://www.stern.nyu.edu/~adamodar/pd...
Post class test: http://www.stern.nyu.edu/~adamodar/pd...
Post class test solution: http://www.stern.nyu.edu/~adamodar/pd...
Webpage for statistics class: http://people.stern.nyu.edu/adamodar/...
YouTube Playlist for this class:    • Statistics 101