Answer: a) Identify response and predictors from the R table. Write the linear regression model

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Chapter4: Linear Functions
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c) If a student scored 82 on the admissions test and was 17 years old at
the time of application, what would be your prediction of their GPA? (
d) If another student scored 97 on the admissions test and was 19 years
old at the time of application, what would be your prediction of their GPA?
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Transcribed Image Text:c) If a student scored 82 on the admissions test and was 17 years old at the time of application, what would be your prediction of their GPA? ( d) If another student scored 97 on the admissions test and was 19 years old at the time of application, what would be your prediction of their GPA? Senior
Question 22
After building the regression model we receive the results shown here:
Call:
1m (formula = gpa - test + age)
Residuals:
1
test
age
Answer:
8
0.00307
2
3
4
-0.16842 0.02851 -0.07939 0.13158 0.07456 0.12807 -0.11798
5
6
Coefficients: oplica
Estimate Std. Error t value Pr (>|t|)
(Intercept) -1.900439 0.984841 -1.930 0.11153
0.025702
0.004937 5.206
0.00345 **
0.182456
0.064412 2.833
0.03656 *
Signif. codes: 01*** 0.001 ** 0.01 0.05. 0.11
Residual standard error: 0.1332 on 5 degrees of freedom
Multiple R-squared: 0.9328,
Adjusted R-squared: 0.9059
F-statistic: 34.71 on 2 and 5 DF, p-value: 0.00117
7
a) Identify response and predictors from the R table. Write the linear
regression model
b) According to this model, what impact would a single point increase in
admissions test score have on the prediction of a student's GPA? How
about a single year increase in age?
Transcribed Image Text:Question 22 After building the regression model we receive the results shown here: Call: 1m (formula = gpa - test + age) Residuals: 1 test age Answer: 8 0.00307 2 3 4 -0.16842 0.02851 -0.07939 0.13158 0.07456 0.12807 -0.11798 5 6 Coefficients: oplica Estimate Std. Error t value Pr (>|t|) (Intercept) -1.900439 0.984841 -1.930 0.11153 0.025702 0.004937 5.206 0.00345 ** 0.182456 0.064412 2.833 0.03656 * Signif. codes: 01*** 0.001 ** 0.01 0.05. 0.11 Residual standard error: 0.1332 on 5 degrees of freedom Multiple R-squared: 0.9328, Adjusted R-squared: 0.9059 F-statistic: 34.71 on 2 and 5 DF, p-value: 0.00117 7 a) Identify response and predictors from the R table. Write the linear regression model b) According to this model, what impact would a single point increase in admissions test score have on the prediction of a student's GPA? How about a single year increase in age?
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