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

This is the third in a series of lecture notes that, tied together in a manual, you might call “practical regression.” The purpose of these notes is to complement the theoretical content of most statistics texts with practical tips based on nearly three decades of experience of the author, along with over a hundred years experience of colleagues who have offered advice. As the title of the “practice of regression” suggests, these notes are a guide to a regression in practice. This technical note explains how to choose the explanatory variables included in the regression. The note begins by explaining the many virtues of parsimony. Sometimes, analysts predict simply because they are in the available data. Predictors including “junk” increases the chances of getting surprising or misleading results. The paper also examines the multicollinearity, a favorite subject in certain kinds of statistics that is usually not a problem in empirical work in the real world. The note concludes by explaining how to work with groups of related variables and describe how? PART F test of joint significance.
by
David Dranove
Source: Kellogg School of Management
9 pages.
Date Posted: April 20, 2012. Prod #: KEL637-PDF-ENG
Regression Practice: Create the model: What are the variables to include the solution of the case

Related Case Solutions:

Practical Regression: Noise Heteroskedasticity and Grouped Data Practical Regression: Discrete Dependent Variables Practical Regression: Causality and Instrumental Variables Practical Regression: Discrete Dependent Variables Practical Regression: Causality and Instrumental Variables Practical Regression: Regression Basics Practical Regression: Regression Basics Practical Regression: Log vs. Linear Specification Practical Regression: Log vs. Linear Specification Modeling Discrete Choice: Categorical Dependent Variables, Logistic Regression, and Maximum Likelihood Estimation