Linear Regression


Linear regression is used to examine how variation in one or more factors is associated with changes in a continuous outcome. It supports comparison of effects across variables, analysis of uneven data, and practical application of the General Linear Model. The focus is on understanding relationships rather than prediction alone.

Technique Overview

Linear Regression

Linear Regression Definition

Linear regression is a statistical technique used to model the relationship between a continuous outcome and one or more explanatory variables by estimating linear effects from observed data. It allows multiple factors to be analysed simultaneously, including continuous and coded categorical variables, and represents a foundational application of the General Linear Model (Kutner et al., 2005; Montgomery et al., 2021; Weisberg, 2005).

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Further Reading

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Linear Regression references (4 of up to 20) *

  • Kutner, M.H. (2005). Applied linear statistical models. Pubblicazione: Boston: Mcgraw-Hill Irwin.
  • Montgomery, D.C., Peck, E.A. and Vining, G.G., 2021. Introduction to linear regression analysis. John Wiley & Sons.
  • Draper, N.R. and Smith, H. (1998). Applied regression analysis. New York: Wiley.
  • Weisberg, S., 2005. Applied linear regression (Vol. 528). John Wiley & Sons.

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