In: Statistics and Probability
Model Summary |
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Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
1 |
.814a |
.662 |
.620 |
7.38577 |
a. Predictors: (Constant), Time (min) |
ANOVAa |
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Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
1 |
Regression |
854.624 |
1 |
854.624 |
15.667 |
.004b |
Residual |
436.397 |
8 |
54.550 |
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Total |
1291.021 |
9 |
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a. Dependent Variable: Grade (%) |
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b. Predictors: (Constant), Time (min) |
Coefficientsa |
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Model |
Unstandardized Coefficients |
Standardized Coefficients |
T |
Sig. |
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B |
Std. Error |
Beta |
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1 |
(Constant) |
25.584 |
13.032 |
1.963 |
.085 |
|
Time (min) |
.407 |
.103 |
.814 |
3.958 |
.004 |
|
a. Dependent Variable: Grade (%) |
A freshman who didn't take the course yet asks about the results from part a: “So, explain this to him / her. Does this mean that if a student wait and turn in the test at the last minute, he / she is going to get a good grade?” Respond as a competent biostatistician, based on your results and your understanding of the conceptual basis of the analysis.