The following example uses the fitness data from Example 74.2. Figure 74.30 shows the parameter estimates and the tables from the SS1, SS2, STB, CLB, COVB, and CORRB options:

proc reg data=fitness;
   model Oxygen=RunTime Age Weight RunPulse MaxPulse RestPulse
         / ss1 ss2 stb clb covb corrb;
run;

The procedure first displays an analysis of variance table (Figure 74.29). The statistic for the overall model is significant, indicating that the model explains a significant portion of the variation in the data.

The procedure next displays parameter estimates and some associated statistics (Figure 74.30). First, the estimates are shown, followed by their standard errors. The next two columns of the table contain the statistics and the corresponding probabilities for testing the null hypothesis that the parameter is not significantly different from zero. These probabilities are usually referred to as -values. For example, the Intercept term in the model is estimated to be 102.9 and is significantly different from zero. The next two columns of the table are the result of requesting the SS1 and SS2 options, and they show sequential and partial sums of squares (SS) associated with each variable. The standardized estimates (produced by the STB option) are the parameter estimates that result when all variables are standardized to a mean of 0 and a variance of 1. These estimates are computed by multiplying the original estimates by the standard deviation of the regressor (independent) variable and then dividing by the standard deviation of the dependent variable. The CLB option adds the upper and lower confidence limits for the parameter estimates; the level can be changed by specifying the ALPHA= option in the PROC REG or MODEL statement.

The final two tables are produced as a result of requesting the COVB and CORRB options (Figure 74.31). These tables show the estimated covariance matrix of the parameter estimates, and the estimated correlation matrix of the estimates.

For further discussion of the parameters and statistics, see the section Displayed Output, and Chapter 4, Introduction to Regression Procedures.