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March 1, 1993Journal of the American Statistical Association5,449 citations

Introduction to Linear Regression Analysis.

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RARenato AssunçãoUniversidade Federal de Ouro PretoPSPaul D. SampsonUniversity of WashingtonDMDouglas C. MontgomeryUniversity of Arizona

Key Points

  • The aim is to provide a comprehensive understanding of linear regression and its various forms and applications.
  • Explains simple and multiple linear regression techniques.
  • Discusses model adequacy checking, diagnosis of leverage and influence.
  • Covers advanced topics such as polynomial regression, multicollinearity, and generalized linear models.
  • Provides frameworks and strategies for model validation and selection.
  • Addresses common issues in regression analysis and solutions.
  • Includes practical exercises and statistical tables for application.

Abstract

Preface. Introduction. Simple Linear Regression. Multiple Linear Regression. Model Adequacy Checking. Transformations and Weighting to Correct Model Inadequacies. Diagnostics for Leverage and Influence. Polynomial Regression Models. Indicator Variables. Variable Selection and Model Building. Multicollinearity. Robust Regression. Introduction to Nonlinear Regression. Generalized Linear Models. Other Topics in the Use of Regression Analysis. Validation of Regression Models. Appendix A. Statistical Tables. Appendix B. Data Sets for Exercises. Appendix C. Supplemental Technical Material. References. Index.

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Cite This Study

Assunção et al. (1993) studied this question.

synapsesocial.com/papers/6a0effd4c12540356222d8b9https://doi.org/10.2307/2290746
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