Practical Data Science using Python - Linear Regression OLS Assumptions and Testing

The video tutorial covers the concept of error terms in linear regression, emphasizing that the population mean of error terms should be zero. It discusses key assumptions such as uncorrelated predictor variables, constant variance (homoscedasticity), and absence of multicollinearity. The tutorial also explains the importance of R-squared and adjusted R-squared values, coefficients, and p-values in model evaluation. Techniques like variance inflation factor (VIF) and recursive feature elimination (RFE) are introduced for optimizing models. Finally, it highlights the significance of residual analysis and probability plots in validating model assumptions.

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