Episode Details

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/pdfiles/Statistics101/Slides/Session5.pdf
Post class test: http://www.stern.nyu.edu/~adamodar/pdfiles/Statistics101/postclass/session5test.pdf
Post class test solution: http://www.stern.nyu.edu/~adamodar/pdfiles/Statistics101/postclass/session5soln.pdf
Webpage for statistics class: http://people.stern.nyu.edu/adamodar/New_Home_Page/webcaststatistics.htm
YouTube Playlist for this class: https://www.youtube.com/playlist?list=PLUkh9m2BorqmXcRzWFbzcjMd7fYErVexF

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