You can use PROC REG in SAS to fit linear regression models.
You can use the following basic syntax to fit a simple linear regression model:
proc reg data = my_data;
model y = x;
run;
This will fit the following linear regression model:
y = b0 + b1x
You can use the following basic syntax to fit a multiple linear regression model:
proc reg data = my_data;
model y = x1 x2 x3;
run;
This will fit the following linear regression model:
y = b0 + b1x1 + b2x2 + b3x3
The following example shows how to use PROC REG to fit a simple linear regression model in SAS along with how to interpret the output.
Example: How to Use PROC REG in SAS
Suppose we have the following dataset that contains information on hours studied and final exam score for 15 students in some class:
/*create dataset*/ data exam_data; input hours score; datalines; 1 64 2 66 4 76 5 73 5 74 6 81 6 83 7 82 8 80 10 88 11 84 11 82 12 91 12 93 14 89 ; run; /*view dataset*/ proc print data=exam_data;
We can use PROC REG to fit a simple linear regression model to this dataset, using hours as the predictor variable and score as the response variable:
/*fit simple linear regression model*/ proc reg data = exam_data; model score = hours; run;
The first table in the output shows a summary of the model fit:
The Parameter Estimates table contains the coefficient estimates for the model.
From this table we can see the fitted regression equation:
Score = 65.33 + 1.98*(hours)
The PROC REG procedure also produces residual plots that we can use to check if the assumptions of the linear regression model are met:
Lastly, the PROC REG procedure produces a scatterplot of the raw data with the fitted regression line overlaid on top:
This plot allows us to visually see how well the regression line fits the data.
Note: You can find the complete documentation for PROC REG here.
Additional Resources
The following tutorials explain how to perform other common tasks in SAS:
How to Use Proc Summary in SAS
How to Use Proc Tabulate in SAS
How to Use Proc Rank in SAS