February 20, 2015

# Linear Regression – How To Do It Properly Pt.2 – The Model

**Model Specification and Evaluation**

In the last post we talked about the maths behind linear regression. We looked at how the model is fitted, how individual coefficient estimates are computed and what their individual properties such as mean and variance are. We have also gone over some important conditions that must be satisfied in order for linear regression to really be an effective and powerful tool for data analysis and we have made a point that unless all of these conditions are met, the OLS linear regression model loses most of its authority and other models often become better alternatives.

In today’s post I would like to continue looking at mathematical due diligence that an analyst needs to do in order to make proper use of linear regression. Specifically I would like to talk about specifying the model – selecting the explanatory variables that should be in the model, omitting the explanatory variables that should be left out and avoiding confusion between causality and correlation. I will also look at ways of evaluating and comparing linear regression models with each other and with other kinds of models.