Single and multivariate linear models

Review of Module 8 of Data Analysis for Social Scientists (MITx, edX) – Single and multivariate linear models

Estimating the parameters of joint distributions can be used for prediction, determining causality and just understanding the world better. In linear regression, the regression coefficients can be estimated by using least squares, least absolute deviations or reverse least squares. By performing an analysis of variance, we can get a measure of the goodness-of-fit of the regression obtained. Linear regressions can also be used for non-linear relationships.

In the lectures, Prof Sara discussed the single and multivariate linear models and their assumptions in details, but I will not get into that here!

I was on the road this week and rushed to get the module done. So, I didn’t quite absorb everything, but I think the general concept of fitting relationships between variables is quite straightforward and the homework was not too challenging (unlike the week on Functions of Random Variables!).

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