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The REG Procedure |

**OUTPUT***<***OUT=**SAS-data-set > keyword=names

< ... keyword=names >**;**

All the variables in the original data set are included in the new data set, along with variables created in the OUTPUT statement. These new variables contain the values of a variety of statistics and diagnostic measures that are calculated for each observation in the data set. If you want to create a permanent SAS data set, you must specify a two-level name (for example,

The OUTPUT statement cannot be used when a TYPE=CORR, TYPE=COV, or TYPE=SSCP data set is used as the input data set for PROC REG. See the "Input Data Sets" section for more details.

The statistics created in the OUTPUT statement are described in this section. More details are contained in the "Predicted and Residual Values" section and the "Influence Diagnostics" section. Also see Chapter 3, "Introduction to Regression Procedures," for definitions of the statistics available from the REG procedure.

You can specify the following options in the OUTPUT statement.

**OUT=***SAS data set*-
gives the name of the new data set. By default, the
procedure uses the DATA
*n*convention to name the new data set. *keyword=names*-
specifies the statistics to include in the output data set
and names the new variables that contain the
statistics. Specify a keyword for each desired statistic
(see the following list of keywords), an equal sign, and
the variable or variables to contain the statistic.

In the output data set, the first variable listed after a keyword in the OUTPUT statement contains that statistic for the first dependent variable listed in the MODEL statement; the second variable contains the statistic for the second dependent variable in the MODEL statement, and so on. The list of variables following the equal sign can be shorter than the list of dependent variables in the MODEL statement. In this case, the procedure creates the new names in order of the dependent variables in the MODEL statement.

For example, the SAS statementsproc reg data=a; model y z=x1 x2; output out=b p=yhat zhat r=yresid zresid; run;

create an output data set named b. In addition to the variables in the input data set, b contains the following variables:- yhat, with values that are predicted values of the dependent variable y
- zhat, with values that are predicted values of the dependent variable z
- yresid, with values that are the residual values of y
- zresid, with values that are the residual values of z

You can specify the following keywords in the OUTPUT statement. See the "Model Fit and Diagnostic Statistics" section for computational formulas.**Keyword****Description**COOKD= *names*Cook's *D*influence statisticCOVRATIO= *names*standard influence of observation on covariance of betas, as discussed in the "Influence Diagnostics" section DFFITS= *names*standard influence of observation on predicted value H= *names*leverage, *x*_{i}(**X**'**X**)^{-1}*x*_{i}'LCL= *names*lower bound of a % confidence interval for an individual prediction. This includes the variance of the error, as well as the variance of the parameter estimates. LCLM= *names*lower bound of a % confidence interval for the expected value (mean) of the dependent variable PREDICTED | P= *names*predicted values PRESS= *names**i*th residual divided by (1-*h*), where*h*is the leverage, and where the model has been refit without the*i*th observationRESIDUAL | R= *names*residuals, calculated as ACTUAL minus PREDICTED RSTUDENT= *names*a studentized residual with the current observation deleted STDI= *names*standard error of the individual predicted value STDP= *names*standard error of the mean predicted value STDR= *names*standard error of the residual STUDENT= *names*studentized residuals, which are the residuals divided by their standard errors UCL= *names*upper bound of a % confidence interval for an individual prediction UCLM= *names*upper bound of a % confidence interval for the expected value (mean) of the dependent variable

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