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2. Regression Analysis

Learn about the relation between random variables in a population and sample, estimation methods, least squares criterion, normality assumptions, and minimizing square residuals. Explore how Population Regression Functions (PRF) and Sample Regression Functions (SRF) are interconnected.

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2. Regression Analysis

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  1. 2. Regression Analysis 2.1 Population and sample regression functions • PRF: relation between r.v.’s in population (unknown) • SRF: relation between r.v.’s in sample (we “estimate” it) To estimate means simply to calculate formula Criterion  least squares (LS)  minimization of sq. residuals We want the SRF to be as close as possible to the PRF, w/o knowing the latter! We will have to make assumptions on the distribution of ‘u’ (errors)  normality

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