The REG procedure is a general SAS procedure for regression analysis. It computes the regression line that fits the data.
PROC REG DATA=dataset-name;
MODEL y-variable=x-variable; ßdefines the model to be fitted.
RUN;
Example:
proc reg;
model
brate=lgnp;
run;
The Statistics of Poverty
and Inequality
The REG
Procedure
Model:
MODEL1
Dependent Variable:
brate birth rate (per 1,000 pop)
Analysis
of Variance
Sum of Mean
Source
DF Squares Square F Value Pr > F
Model
1 9152.56716 9152.56716 105.28
<.0001
Error
89 7737.37042 86.93675
Corrected Total 90 16890
Root MSE 9.32399 R-Square 0.5419
Dependent Mean 29.46044 Adj R-Sq 0.5367
Coeff Var 31.64918
Parameter Estimates
Parameter Standard
Variable Label DF Estimate Error t Value
Pr > |t|
Intercept 1 75.52126 4.59430 16.44
<.0001
lgnp
1 -6.13194 0.59762 -10.26
<.0001
The part in bold font, is the output of the REG procedure that we are interested in.
Look under parameter estimate for the values of the intercept and the slope. In the regression line y=a+bx: a is the intercept value and b is the estimate associated to the x-variable.
For example,
PLOT predicted.*residual.;
generates one plot of the predicted values by the residuals for each dependent variable in the MODEL statement. These statistics can also be plotted against any of the variables in the VAR or MODEL statements.
Possible keywords are (note the period after the keyword):
Predicted. (or pred. or p.) = predicted values;
Residual. (or r.) = residuals;
Student. = studentized residuals;
Npp. = normal probability plot;
Specialized plots are requested with special options: the PRED option plots the 95% prediction intervals for the predicted values of Y (using the root mean square error)
PROC REG <DATA=dataset-name>;
MODEL yvar=xvar;
PLOT yvar*xvar/nostat; ß draw scatter plot and
regression line
PLOT residual.*xvar
residual.*predicted.; ßresidual plots to check for linear assumption and outliers
PLOT nnp.*residual.; ß probability plot for the
residuals (to check for normality)
PLOT yvar*xvar/PRED; ß draw scatter plot &
upper and lower prediction
bounds.
RUN;
Example (cont.)
PROC REG;
MODEL brate=lgnp;
PLOT brate*lgnp/nostat;
PLOT residual.*predicted.
PLOT nnp.*residual.;
PLOT brate*lgnp/PRED;
RUN;