Which of the following is used to calculate the R-squared for a regression model?

The R-squared for a regression model is calculated using the following formula:

R-squared = 1 - (SSR/SST)

Where:

• SSR (Sum of Squared Residuals) is the sum of the squares of the model's residual errors.
• SST (Total Sum of Squares) is the total variation in the dependent variable.

Here are the steps to calculate R-squared:

1. Calculate the residuals: The residuals of a model are the difference between the observed values of the dependent variable and the predicted values from the model.

2. Square the residuals: To calculate SSR, square each of the residuals.

3. Sum the squared residuals: Add up all the squared residuals to get the SSR.

4. Calculate the total sum of squares: Subtract the mean of the dependent variable from each of the observed values, square the result, and then sum these squares to get the SST.

5. Substitute SSR and SST in the R-squared formula: Finally, substitute the SSR and SST values into the R-squared formula and calculate the result.

The resulting value, R-squared, represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model. It ranges from 0 to 1, with 1 indicating that the independent variables perfectly predict the dependent variable.