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Introduction to Simple 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 previous chapters, we have had data for which there has been a dependent variable (Y ) and an independent variable (X – even though, to be consistent with the notation that is close to universal in the field of experimental design, we have been using factor names, A, B, etc., or “column factor” and “row factor,” instead of, literally, the letter X ). The latter has been treated mostly as a categorical variable, whether actually numerical/metric or not. Often, we have had more than one independent variable. Assuming only one independent variable, if we want to say it this way (and we do!), we can say that we have had n (X, Y ) pairs of data, where n is the total number of data points. With more than one independent variable, we can say that we have n (X 1, X 2, …, Y ) data points.

Suggested Citation

  • Paul D. Berger & Robert E. Maurer & Giovana B. Celli, 2018. "Introduction to Simple Regression," Springer Books, in: Experimental Design, edition 2, chapter 0, pages 483-503, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-64583-4_14
    DOI: 10.1007/978-3-319-64583-4_14
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