Basic Analysis and Graphing - SAS

Chapter 4 Performing Bivariate Analysis 105
Fit Line and Fit Polynomial
Figure 4.13 Examples of Parameter Estimates Reports for Linear and Polynomial Fits
Table 4.7 Description of the Parameter Estimates Report
Term
Estimate
Std Error
t Ratio
Prob>|t|
Lists the name of each parameter in the requested model. The intercept is a constant term
in all models.
Lists the parameter estimates of the linear model. The prediction formula is the linear
combination of these estimates with the values of their corresponding variables.
Lists the estimates of the standard errors of the parameter estimates. They are used in
constructing tests and confidence intervals.
Lists the test statistics for the hypothesis that each parameter is zero. It is the ratio of the
parameter estimate to its standard error. If the hypothesis is true, then this statistic has a
Student’s t-distribution.
Lists the observed significance probability calculated from each t-ratio. It is the probability
of getting, by chance alone, a t-ratio greater (in absolute value) than the computed value,
given a true null hypothesis. Often, a value below 0.05 (or sometimes 0.01) is interpreted
as evidence that the parameter is significantly different from zero.
To reveal additional statistics, right-click in the report and select the Columns menu. Statistics not shown
by default are as follows:
Lower 95%
Upper 95%
The lower endpoint of the 95% confidence interval for the parameter estimate.
The upper endpoint of the 95% confidence interval for the parameter estimate.
Std Beta The standardized parameter estimate. It is useful for comparing the effect of X variables that are
measured on different scales. See “Statistical Details for the Parameter Estimates Report” on page 126.
VIF
The variance inflation factor.
Design Std Error The design standard error for the parameter estimate. See “Statistical Details for the
Parameter Estimates Report” on page 126.