Setting the Model Options

specifies the significance level to use for the construction of confidence intervals.

You can choose to include the default statistics in the results or choose to include additional statistics.

displays the standardized regression coefficients. A standardized regression coefficient is computed by dividing a parameter estimate by the ratio of the sample standard deviation of the dependent variable to the sample standard deviation of the regressor.

displays the upper and lower confidence limits for the parameter estimates.

displays the sequential sums of squares (Type I SS) along with the parameter estimates for each term in the model.

displays the partial sums of squares (Type II SS) along with the parameter estimates for each term in the model.

displays the squared partial correlation coefficients computed by using Type I and Type II sums of squares.

displays the squared semipartial correlation coefficients computed by using Type I and Type II sums of squares. This value is calculated as sum of squares divided by the corrected total sum of squares.

requests a detailed analysis of the influence of each observation on the estimates and the predicted values.

requests an analysis of the residuals. The results include the predicted values from the input data and the estimated model, the standard errors of the mean predicted and residual values, the studentized residual, and Cook’s D statistic to measure the influence of each observation on the parameter estimates.

calculates predicted values from the input data and the estimated model.

specifies whether to compute and compare the least squares means of fixed effects.

specifies the effects that you want to compare. You specified these effects on the Model tab.

requests a multiple comparison adjustment for the p-values and confidence limits for the differences of the least squares means. Here are the valid methods: Bonferroni, Nelson, Scheffé, Sidak, and Tukey.

requests that a t type confidence interval be constructed for each of the least squares means with a confidence level of 1 – number. The value of number must be between 0 and 1. The default value is 0.05.

requests a detailed analysis of collinearity among the regressors. This includes eigenvalues, condition indices, and decomposition of the variances of the estimates with respect to each eigenvalue.

produces tolerance values for the estimates. Tolerance for a variable is defined as , where R square is obtained from the regression of the variable on all other regressors in the model.

produces variance inflation factors with the parameter estimates. Variance inflation is the reciprocal of tolerance.

performs a test to confirm that the first and second moments of the model are correctly specified.

displays the estimated asymptotic covariance matrix of the estimates under the hypothesis of heteroscedasticity and heteroscedasticity-consistent standard errors of parameter estimates.

By default, several diagnostic plots are included in the results. You can also specify whether to include plots of the residuals for each explanatory variable.

plots studentized residuals by predicted values. If you select the Label extreme points option, observations with studentized residuals that lie outside the band between the reference lines are deemed outliers.

plots the DFFITS statistic by observation number. If you select the Label extreme points option, observations with a DFFITS statistic greater in magnitude than are deemed influential. The number of observations used is n, and the number of regressors is p.

produces panels of DFBETAS by observation number for the regressors in the model. You can view these plots as a panel or as individual plots. If you select the Label extreme points option, observations with a DFBETAS statistic greater in magnitude than are deemed influential for that regressor. The number of observations used is n.

identifies the extreme values on each different type of plot.

produces a scatter plot of the data overlaid with the regression line, confidence band, and prediction band for models with a single continuous variable. The intercept is excluded. When the number of points exceeds the value for the Maximum number of plot points option, a heat map is displayed instead of a scatter plot.

produces a scatter plot of the observed values versus the predicted values.

produces partial regression plots for each regressor. If you display these plots in a panel, there is a maximum of six regressors per panel.

specifies the maximum number of points to include in each plot.