The document discusses fitting linear regression models to data with multiple predictors. It loads data, separates the response and predictor matrices, defines a function to calculate R-squared and sigmahat for all 2-variable models and ranks them by R-squared, and applies this function. The top 3 models are examined, with the best having the lowest sigmahat and highest R-squared, while the worst has the highest sigmahat and lowest R-squared. However, as the best model only has an R-squared of 0.479, there may be better fitting models involving more variables.

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