Multiple Linear Regression

Paul D. Berger, Robert E. Maurer and Giovana B. Celli
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Paul D. Berger: Bentley University
Robert E. Maurer: Boston University, Questrom School of Business
Giovana B. Celli: Cornell University

Chapter Chapter 15 in Experimental Design, 2018, pp 505-532 from Springer

Abstract: Abstract In the previous chapter, we discussed situations where we had only one independent variable (X ) and evaluated its relationship with a dependent variable (Y ). This chapter goes beyond that and deals with the analysis of situations where we have more than one X (predictor) variable, using a technique called multiple regression. Similarly to simple regression, the objective here is to specify mathematical models that can describe the relationship between Y and more than one X and that can be used to predict the outcome at given values of the predictors. As we did in Chap. 14 , we focus on linear models.

Date: 2018
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DOI: 10.1007/978-3-319-64583-4_15

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