Furthermore, the t values and p-values for the slope parameters are equivalent because these statistics are scale- and translation-invariant. The parameter estimates for the standardized data are equal to the STB estimates for the original data. METHOD=STD standardizes variables according to StdX = (X - mean(X)) / std(X) */ proc stdize data=Sashelp.Class out=ClassStd method= std OPREFIX SPREFIX= Std Standardized variables have the names 'StdX' where X was the name of the original variable.
* Put original and standardized variables into the output data set. The following call to PROC REG uses the STB option to compute the standardized parameter estimates for a model that predicts the weights of 19 students from heights and ages:
#STB MEANING HOW TO#
The following example uses continuous response and explanatory variables, but there is a SAS Usage Note that describes how to standardize classification variables. This section uses PROC STDIZE and PROC REG to "prove by example" that the standardized regression estimates for data are equal to the estimates that you obtain by standardizing the data. One of my college physics professors used to smile and say "I will now prove this by example" when he wanted to demonstrate a fact without proving it mathematically. It also derives how regression coefficients change after a linear transformation of the variables. This article uses SAS to demonstrate how parameter estimates for the original variables are related to parameter estimates for standardized variables. The SAS documentation for the STB option states, "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." Although correct, this definition does not provide an intuitive feeling for how to interpret the standardized regression estimates. A SAS programmer recently asked how to interpret the "standardized regression coefficients" as computed by the STB option on the MODEL statement in PROC REG and other SAS regression procedures.
0 kommentar(er)
