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Regression Modeling

In: An Introduction to Computational Science

Author

Listed:
  • Allen Holder

    (Rose-Hulman Institute of Technology)

  • Joseph Eichholz

    (Rose-Hulman Institute of Technology)

Abstract

We learned in Chap. 3 how to calculate and assess model parameters for a linear regression model. However, we did not consider which variables a model should include, and it is this question that we approach here. The process of identifying a collection of independent variables to gain an accurate statistical analysis of a response variable is commonly, and simply, referred to as “model building” model building (regression) in the world of statistics. The act of model building combines intuition and computational skill and is an artful application of mathematics. Numerous appropriate models are often inferred from the same data, and indeed, disparate models are regularly disputed among experts. What is important is to be able to identify a model’s benefits and weaknesses so that its strengths can be leveraged and its faults avoided.

Suggested Citation

  • Allen Holder & Joseph Eichholz, 2019. "Regression Modeling," International Series in Operations Research & Management Science, in: An Introduction to Computational Science, chapter 0, pages 431-445, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-15679-4_13
    DOI: 10.1007/978-3-030-15679-4_13
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