S, R and adjusted R are measures of how well the model fits the data. These values can help you select the model with the best fit.

·    S is measured in the units of the response variable and represents the standard distance data values fall from the fitted values. For a given study, the better the model predicts the response, the lower S is.

·    R (R-Sq) describes the amount of variation in the observed response values that is explained by the predictor(s). R always increases with additional predictors. For example, the best five-predictor model will always have a higher R than the best four-predictor model. Therefore, R is most useful when comparing models of the same size.

·    Adjusted R is a modified R that has been adjusted for the number of terms in the model. If you include unnecessary terms, R can be artificially high. Unlike R, adjusted R may get smaller when you add terms to the model. Use adjusted R to compare models with different numbers of predictors.

For the door lock data, S is 1.10672, R is 19.23%, and the adjusted R equals 12.02%. The low adjusted R value indicates that the univariate model for quality does not fit the data well.

If you are comparing different door lock quality models, then you generally look for models that minimize S and maximize the two R values.