Regression Analysis

Objective

To measure in precise quantitative terms the relationship between one dependent variable and one or more independent variables.

Examples/Applications

• Prediction
• Analysis
• Developing performance standards
• Screening variables
• Media/trade program impact
• Matching samples

Assumptions

• Causal, or at least meaningful, relationships
• Linearity
• Independent variables are independent (low collinearity)
• Heteroscedasticity (uncorrelated error terms)
• Relationship can be explained in terms of variance around a line estimate

Mechanics

• Estimating equation
• Total variance
• Explained and unexplained variance
• Coefficient of determination and correlation
• beta (slope) and b (intercept)
• Partial correlations
• Dummy variables
• Recursive regressions (stepwise/best fit)

Seminal Articles/Texts

• A.R. Baagaley, Intermediate Correlational Methods, John Wiley & Sons, 1964

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