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What is Key Assumptions of Linear Regression about?
This document outlines the key assumptions of linear regression: linear relationship between variables, multivariate normality of variables, little or no multicollinearity, no autocorrelation, and homoscedasticity of residuals. It provides examples and tests to check if data meets each assumption, including scatter plots to check for linearity and homoscedasticity, and the Durbin-Watson test for autocorrelation. Sample size guidelines are also presented.
- Author
- Waqar Ahmad
- Language
- EN