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 0.942587, R is 44.40%, and the adjusted R equals 39.43%. If you are comparing different door lock usability models, then you generally look for models that minimize S and maximize the two R values.