Below is the output from the regression procedure from the statistical package SPSS. Note that the tests of the coefficients use t tests rather than an F test. However, if the t values are squared then the results are identical to those computed in the text. For example, if you square the t of 2.047 for SAT you obtain 4.19 as calculated above. Note that the p values match.
Model Summary 

Model 
R 
R Square 
Adjusted R Square 
Std. Error of the Estimate 


1 
.789^{a} 
.623 
.616 
.27718 
a. Predictors: (Constant), SAT, high_GPA 
ANOVA^{b} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
12.961 
2 
6.481 
84.349 
.000a 
Residual 
7.837 
102 
.077 



Total 
20.798 
104 




a. Predictors: (Constant), SAT, high_GPA 

b. Dependent Variable: univ_GPA 
Coefficients^{a} 

Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 

B 
Std. Error 
Beta 

1 
(Constant) 
.540 
.318 

1.699 
.092 
high_GPA 
.541 
.084 
.625 
6.465 
.000 

SAT 
.001 
.000 
.198 
2.047 
.043 

a. Dependent Variable: univ_GPA 