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Multiple Linear Regression

In: Experimental Design

Author

Listed:
  • Paul D. Berger

    (Bentley University)

  • Robert E. Maurer

    (Boston University, Questrom School of Business)

  • Giovana B. Celli

    (Cornell University)

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.

Suggested Citation

  • Paul D. Berger & Robert E. Maurer & Giovana B. Celli, 2018. "Multiple Linear Regression," Springer Books, in: Experimental Design, edition 2, chapter 0, pages 505-532, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-64583-4_15
    DOI: 10.1007/978-3-319-64583-4_15
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