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 |
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Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|
|
1 |
.789a |
.623 |
.616 |
.27718 |
a. Predictors: (Constant), SAT, high_GPA |
ANOVAb |
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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 |
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b. Dependent Variable: univ_GPA |
Coefficientsa |
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Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
B |
Std. Error |
Beta |
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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 |