6 Multiple Variable Regression Concepts and Omitted Variable Bias


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This post starts the new chapter in our econometrics course. From now on, we move to regression of several variables, instead of just one variable. Once again we derive OLS estimates or normal equations in a multiple variable model ( precisely, 3 variable model).

Then, we pick up hypothesis testing in multiple linear regression model. How to test several coefficients jointly? How to test linear combination of coefficients?

Last video, explains, yet another very important concept : omitted variable bias. What happens, if you remove one very important variable from the regression model? How does that affect OLS estimates, are they still unbiased? Continue reading

6 Hypothesis Testing (Simple Linear Regression) videos


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Here we present the concept of Hypothesis testing. We start with the simple linear regression model and test for the following:

  • Statistical significance of one variable
  • Joint statistical significance of all variables taken together

We begin by introducing hypothesis testing, then explaining one tailed and two tailed t tests and end this discussion by explaining test for goodness of fit Continue reading