Final R-squared Stepwise Regression

[LABRADOR]

Hello SAS Community, 

I was able to get the correct answer for this question using SAS Enterprise Guide; However, I'd like to know how this can be done through code rather than the point-and-click method. Could someone please tell me how to find R-squared for the final model?  

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Using x22 as the dependent variable and x7 to x21 as predictor variables using stepwise regression with a .05 enter level and .05 leave level, what is the R-squared for the final model?

x7	x21	x22
3.9	8.4	65.1
2.7	7.5	67.1
3.4	9	72.1
3.3	7.2	40.1
3.4	9	57.1
2.8	6.1	50.1
3.7	7.2	41.1
3.3	7.7	56.1
3.6	8.2	56.1
4.5	6.7	59.1
3.2	8.4	68.1
4.9	6.6	53.1
5.6	7.9	58.1
3.9	8.2	72.1
4.5	7.6	62.1
3.2	7.1	71.1
4	7.2	50.1
4.1	8.2	58.1
3.4	7.9	55.1
4.5	8.8	67.1
3.8	7	50.1
5.7	9.9	70.1
3.6	8.1	60.1
2.4	8	65.1
4.1	5.5	55.1
3.6	7	58.1
3	7	70.1
3.3	5.6	55.1
3	7.2	70.1
3.6	6.2	52.1
3.4	7.1	44.1
2.5	6.2	51.1
3.7	7.6	44.1
3.3	9	62.1
4	6.7	54.1
3.2	7.1	51.1
3.4	7.2	57.1
4.1	9.9	77.1
3.6	7.6	65.1
4.9	5.8	53.1
3.4	8.4	61.1
3.8	7.9	61.1
5.1	7.6	72.1
5.1	8.4	55.1
2.5	6.5	65.1
4.1	7.7	58.1
4.3	8	67.1
3.8	7.1	60.1
3.7	8.5	67.1
3.9	7.6	61.1
3.6	7.2	48.1
2.7	8.2	67.1
2.5	9	66.1
3.4	7.2	44.1
3.3	8.1	62.1
3.8	8.9	59.1
5.1	8.8	74.1
3.6	7.5	58.1
4.3	7	67.1
2.8	8.5	61.1
3.2	7.2	71.1
3.8	8.8	63.1
3.9	8	44.1
2.2	8.1	47.1
3.6	7.1	48.1
3.8	9	60.1
4	6.2	50.1
3.7	8.2	48.1
3.5	5.8	51.1
3.6	8	58.1
4.5	7.7	67.1
3.2	7	43.1
4.3	7.9	66.1
3.7	9.8	66.1
3.9	8.4	65.1
3	8.9	63.1
3.6	7.5	49.1
3.8	8	61.1
3.5	8.1	72.1
3.4	7.6	44.1
3	8.8	63.1
3.2	8	68.1
2.9	8.5	53.1
3.2	6.5	37.1
2.6	7.7	52.1
3.5	7.2	51.1
3.6	6	48.1
2.6	8.2	52.1
3.6	7.4	59.1
5.5	9.3	59.1
3.7	7.9	58.1
4.2	6.5	51.1
3.9	8.6	72.1
3.5	8.9	72.1
3.8	8.4	59.1
4.8	8.1	50.1
3.4	7.2	48.1
3.2	7.7	51.1
4.9	7.4	61.1
3	7	57.1
3.6	6.1	52.1
5	7.1	71.1
4.7	7.6	59.1
5.6	9	58.1
4.3	8.9	66.1
3.4	7.5	61.1
4.1	9.3	77.1
4	8	62.1
3.7	7.6	61.1
3.2	7.1	43.1
5	8.1	66.1
3.5	7.9	69.1
3.6	7.2	65.1
4.3	7.7	65.1
3.8	7.9	63.1
4.7	6.9	59.1
5	9.5	71.1
3.6	7.5	46.1
4.1	8	45.1
2.5	7.1	51.1
5.1	8.8	74.1
3	8	57.1
2.2	7.7	47.1
2.5	8.2	66.1
4.3	6.5	52.1
3.7	8.1	54.1
3.9	8.1	61.1
3.6	6.9	46.1
5.7	9.3	70.1
2.8	6.2	47.1
3.6	8	60.1
2.5	7.1	65.1
3.2	6.5	37.1
3.8	7.1	53.1
2.5	8.2	51.1
4.1	7	75.1
2.8	6.7	47.1
4.9	7.5	61.1
4.2	7.4	51.1
3.6	7.4	49.1
3.4	7.9	55.1
2.8	8	61.1
3.4	8	72.1
3.8	8.4	72.1
4.2	8.8	66.1
4.2	7.9	66.1
3.8	6	53.1
3.7	8.2	58.1
5.1	8.4	55.1
4.1	7.4	55.1
4.3	8	65.1
3.3	6.6	56.1
4.3	7.6	52.1
3.5	7.5	69.1
5.1	7.1	55.1
2.8	7.9	61.1
3.6	7.6	48.1
3.4	7.1	48.1
3.7	7.6	48.1
3.6	8.2	59.1
3.7	6.9	44.1
4.5	8.1	62.1
3.7	7.6	54.1
3.6	8.4	53.1
3.6	7.4	53.1
4.1	7.9	45.1
3.4	7.2	44.1
3.7	7.6	41.1
2.9	6.7	53.1
4	7.4	54.1
3.3	6.2	55.1
2.6	7.5	59.1
3.7	7.4	66.1
4.8	7.9	50.1
5.1	6.5	55.1
3.7	8.6	67.1
2.4	8.6	65.1
5	8	66.1
2.6	8.1	59.1
5.7	8.2	70.1
2.5	7.2	51.1
4.1	8.4	75.1
5.7	9.4	70.1
5.1	9.4	72.1
2.8	7.5	61.1
3.8	6.6	72.1
2.8	4.3	50.1
3.6	6.6	48.1
3.6	7.4	56.1
3.7	7.1	61.1
3.9	6.7	44.1
4.5	6.7	59.1
3.6	7.2	48.1
3.8	7.1	50.1
3.3	6	40.1
3.6	8.4	58.1
4	8.6	62.1
5	7.9	66.1
5.5	7.6	59.1
5	8.5	66.1

[PaigeMiller · Accepted solution]

Use PROC REG. There are examples at the link. Use SELECTION=STEPWISE in the model statement.

Also, I haven't used Enterprise Guide in a long time, but it should be able to provide you with the exact SAS code.

[LABRADOR]

Thank you!