Practical Regression: Building Your Model: What Variables to Include Harvard Case Solution & Analysis

This is the third in a series of lectures, which, if linked together in the book, could be entitled "Practical Regression." Goal score in addition to the theoretical content of most statistics texts with practical advice based on nearly three decades of experience of the author, along with more than a hundred years experience of colleagues who offered advice. As the title "Practical Regression" suggests, these notes guide performing the regression in practice. This technical note explains how to select the predictors are included. The note begins by explaining the many virtues of thrift. Sometimes analysts include predictors, simply because they are in the data. Including this "junk" predictors increases the chances of getting confusing or misleading results. The note also examines the multicollinearity, a favorite theme in some statistics classes, which is rarely a problem in the real world of empirical work. The note concludes by explaining how to work with groups of related variables and describes how to implement a partial F test for joint significance. "Hide
by David Dranove Source: Kellogg School Management 9 pages. Publication date: April 20, 2012. Prod. #: KEL637-PDF-ENG

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