Model summary statistics for Stability Study

Find definitions and interpretation guidance for every statistic in the Model summary table.

In This Topic

S
R-sq
R-sq (adj)
R-sq (pred)

S

S represents the standard deviation of the distance between the data values and the fitted values. S is measured in the units of the response.

Interpretation

Use S to assess how well the model describes the response. S is measured in the units of the response variable and represents how far the data values fall from the fitted values. The lower the value of S, the better the model describes the response. However, a low S value by itself does not indicate that the model meets the model assumptions. You should check the residual plots to verify the assumptions.

For example, you work for a potato chip company that examines the factors that affect the percentage of crumbled potato chips per container. You reduce the model to the significant predictors, and S is calculated as 1.79. This result indicates that the standard deviation of the data points around the fitted values is 1.79. If you are comparing models, values that are lower than 1.79 indicate a better fit, and higher values indicate a worse fit.

R-sq

R² is the percentage of variation in the response that is explained by the model. It is calculated as 1 minus the ratio of the error sum of squares (which is the variation that is not explained by model) to the total sum of squares (which is the total variation in the model).

Interpretation

Use R² to determine how well the model fits your data. The higher the R² value, the better the model fits your data. R² is always between 0% and 100%.

You can use a fitted line plot to graphically illustrate different R² values. The first plot illustrates a simple regression model that explains 85.5% of the variation in the response. The second plot illustrates a model that explains 22.6% of the variation in the response. The more variation that is explained by the model, the closer the data points fall to the fitted regression line. Theoretically, if a model could explain 100% of the variation, the fitted values would always equal the observed values and all of the data points would fall on the fitted line. However, even if R² is 100%, the model does not necessarily predict new observations well.

Consider the following issues when interpreting the R² value:

R² always increases when you add additional predictors to a model. For example, the best five-predictor model will always have an R² that is at least as high as the best four-predictor model. Therefore, R² is most useful when you compare models of the same size.
Small samples do not provide a precise estimate of the strength of the relationship between the response and predictors. For example, if you need R² to be more precise, you should use a larger sample (typically, 40 or more).
Goodness-of-fit statistics are just one measure of how well the model fits the data. Even when a model has a desirable value, you should check the residual plots to verify that the model meets the model assumptions.

R-sq (adj)

Adjusted R² is the percentage of the variation in the response that is explained by the model, adjusted for the number of predictors in the model relative to the number of observations. Adjusted R² is calculated as 1 minus the ratio of the mean square error (MSE) to the mean square total (MS Total).

Interpretation

Use adjusted R² when you want to compare models that have different numbers of predictors. R² always increases when you add a predictor to the model, even when there is no real improvement to the model. The adjusted R² value incorporates the number of predictors in the model to help you choose the correct model.

For example, you work for a potato chip company that examines the factors that affect the percentage of crumbled potato chips per container. You receive the following results as you add the predictors in a forward stepwise approach.

Model	% Potato	Cooling rate	Cooking temp	R²	Adjusted R²
1	X			52%	51%
2	X	X		63%	62%
3	X	X	X	65%	62%

The first model yields an R² of more than 50%. The second model adds cooling rate to the model. Adjusted R² increases, which indicates that cooling rate improves the model. The third model, which adds cooking temperature, increases the R² but not the adjusted R². These results indicate that cooking temperature does not improve the model. Based on these results, you consider removing cooking temperature from the model.

R-sq (pred)

Predicted R² is calculated with a formula that is equivalent to systematically removing each observation from the data set, estimating the regression equation, and determining how well the model predicts the removed observation. The value of predicted R² ranges between 0% and 100%. (While the calculations for predicted R² can produce negative values, Minitab displays zero for these cases.)

Interpretation

Use predicted R² to determine how well your model predicts the response for new observations. Models that have larger predicted R² values have better predictive ability.

A predicted R² that is substantially less than R² may indicate that the model is over-fit. An over-fit model occurs when you add terms for effects that are not important in the population. The model becomes tailored to the sample data and, therefore, may not be useful for making predictions about the population.

Predicted R² can also be more useful than adjusted R² for comparing models because it is calculated with observations that are not included in the model calculation.

For example, an analyst at a financial consulting company develops a model to predict future market conditions. The model looks promising because it has an R² of 87%. However, the predicted R² is 52%, which indicates that the model may be over-fit.