What is used to assess the overall accuracy of a linear regression model? R-squared p-value Mean absolute error F-statistic

All of the options you provided can be used to assess the accuracy of a linear regression model, but they each measure different aspects of the model's performance. Here's a brief explanation of each:

1. R-squared: This is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model. If the R-squared of a model is 0.50, then approximately half of the observed variation can be explained by the model's inputs.

2. P-value: In the context of a linear regression model, the p-value is used to determine the statistical significance of each coefficient in the model. A low p-value (< 0.05) indicates that the coefficient is statistically significant.

3. Mean Absolute Error (MAE): This is a measure of errors between paired observations expressing the same phenomenon. It's the average over the test sample of the absolute differences between prediction and actual observation where all individual differences have equal weight.

4. F-statistic: This is a good indicator of whether there is a relationship between our predictor and the response variable. The further the F-statistic is from 1 the better it is. If the p-value associated with the F-statistic is small (less than say 0.05), there is a good relationship.

In summary, while all of these can be used to assess the accuracy of a linear regression model, they each provide different insights and are used in different contexts. The best one to use depends on what exactly you want to measure or test in your model.