Question from Mathan

Hi,

Anyone aware of the following why and how to calculate the following
things in regression....

1. R-square,
2. Adjusted R-square
3. VIF
4. Beta weights

R2 (R-sq)

Coefficient of determination; indicates how much variation in the response is explained by the model. The higher the R2 , the better the model fits your data. The formula is:

Adjusted R2

Accounts for the number of predictors in your model and is useful for comparing models with different numbers of predictors. The formula is:

Variance inflation factor (VIF)


Used to detect multicollinearity (correlated predictors). VIF measures how much the variance of an estimated regression coefficient increases if your predictors are correlated. Minitab calculates VIF by regressing each predictor on the remaining predictors and noting the R2 value. For predictor x1, the VIF is:

beta weights??? need some time.