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December 1, 1980Journal of the American Statistical Association3,234 citations

Regression Analysis by Example.

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JKJan KmentaSCSamprit ChatterjeeBPBertram Price

Key Points

  • To summarize and evaluate different regression analysis techniques and their applications in data modeling.
  • Discusses simple and multiple linear regression techniques.
  • Explores regression diagnostics for model violations and variable selection procedures.
  • Reviews logistic regression and its applications, including transformations and weighting methods.
  • Identifies key techniques for detection of model violations in regression analysis.
  • Highlights the importance of variable selection in improving model accuracy.
  • Discusses strategies to address issues such as collinearity and correlated errors in regression models.

Abstract

Simple Linear Regression Multiple Linear Regression Regression Diagnostics: Detection of Model Violations Qualitative Variables as Predictors Transformation of Variables Weighted Least Squares The Problem of Correlated Errors Analysis of Collinear Data Biased Estimation of Regression Coefficients Variable Selection Procedures Logistic Regression Appendix References Index.

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Cite This Study

Kmenta et al. (1980) studied this question.

synapsesocial.com/papers/6a0dbbfbcecdf5fb20ba9565https://doi.org/10.2307/2287213
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