Abstract: Regression analysis is at the very heart of applied statistics. The goal of regression analysis is to make predictions on a continuous response variable based on one or more predictor variables. This chapter begins with a discussion on how linear regression works, and the differences between simple linear regression and multiple regression. It helps the reader to understand what least-squares accomplishes, and why it guarantees to minimize squared error, not necessarily make it small for any given data set. The chapter also helps the reader to understand the difference between R-squared and adjusted R-squared. It lists the categories of assumptions that need to be verified in regression. The technique of hierarchical regression is popular among social scientists in testing mediational hypotheses. Best subset selection involves fitting separate regressions for the pool of predictors. The chapter also discusses variance inflation factor which is computed for each predictor entered into the collinear model.
Publication Year: 2020
Publication Date: 2020-03-27
Language: en
Type: other
Indexed In: ['crossref']
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Cited By Count: 1
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