You can use PROC REG in SAS to fit linear regression models.

You can use the following basic syntax to fit a :

proc regdata = 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 :

proc regdata = 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 printdata=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 :

/*fit simple linear regression model*/
proc regdata = 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 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 .

The following tutorials explain how to perform other common tasks in SAS: