PROC ORTHOREG displays the parameter estimates and associated statistics. These include the following:
overall model analysis of variance, including the error mean square, which is an estimate of (the variance of the true errors), and the overall F test for a model effect.
root mean square error, which is an estimate of the standard deviation of the true errors. It is calculated as the square root of the mean squared error.
R square () measures how much variation in the dependent variable can be accounted for by the model. R square, which can range from 0 to 1, is the ratio of the sum of squares for the model to the corrected total sum of squares. In general, the larger the value of R square, the better the model’s fit.
estimates for the parameters in the linear model
The table of parameter estimates consists of the following:
the terms used as regressors, including the intercept.
degrees of freedom (DF) for the variable. There is one degree of freedom for each parameter being estimated unless the model is not full rank.
estimated linear coefficients.
estimates of the standard errors of the parameter estimates.
the critical t values for testing whether the parameters are This is computed as the parameter estimate divided by its standard error.
the two-sided p-value for the t test, which is the probability that a t statistic would obtain a greater absolute value than that observed given that the true parameter is zero.