Process / pipelinepredictive-modeling
Multiple Regression Analysis
Multiple regression analysis is a statistical method for modeling the relationship between a continuous dependent variable and two or more independent variables (predictors). Originating from Gauss's early 19th-century work and formalized by Draper and Smith (1966), it estimates linear equations predicting outcomes from multiple predictors while accounting for confounding relationships, making it indispensable in epidemiology, economics, psychology, and clinical research.
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Sources
- Draper, N. R., & Smith, H. (1966). Applied Regression Analysis. John Wiley & Sons. DOI: 10.1002/9781118625881 ↗
- Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (1992). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum. DOI: 10.4324/9780203771662 ↗
- Marquardt, D. W. (1980). You should standardize the independent variables in your regression models. Discussion of a paper by G. David Knottnerus. Journal of the American Statistical Association, 75(369), 87–91. DOI: 10.1080/01621459.1980.10477466 ↗