Summary
Contents
Subject index
Damodar N. Gujarati’s Linear Regression: A Mathematical Introduction presents linear regression theory in a rigorous, but approachable manner that is accessible to students in all social sciences. This concise title goes step-by-step through the intricacies, and theory and practice of regression analysis. The technical discussion is provided in a clear style that doesn’t overwhelm the reader with abstract mathematics. End-of-chapter exercises test mastery of the content and advanced discussion of some of the topics is offered in the appendices.
The Classical Linear Regression Model (CLRM)
The Classical Linear Regression Model (CLRM)
In Chapter 1, we showed how we estimate an LRM by the method of least squares. As noted in Chapter 1, estimation and hypothesis testing are the twin branches of statistical inference. Based on the OLS, we obtained the sample regression, such as the one shown in Equation (1.40). This is of course a sample regression function (SRF) because it is based on a specific sample drawn randomly from the purported population. What can we say about the true population regression function (PRF) from the SRF? In practice, we do not observe the PRF and have to “guess” it from the SRF. To obtain the best possible guess, we need a framework, which ...
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