4. The following output from R presents the results from computing a linear model. In our data example we are interested to study the relationship between students' academic performance api00 with variable enroll which is the number of students in the school. Call: Im (formula = api00 - enrol1, data = d) Residuals: Min 10 Median 30 Мах -285.50 -112.55 -6.70 95.06 389.15 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 744.25141 15.93308 46.711 < 2e-16 *** enroll -0.19987 0.02985 -6.695 7.34e-11 *** Signif. codes: 0 '*** ' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' 1 %3D Residual standard error: 135 on 398 degrees of freedom Multiple R-squared: 0.1012, Adjusted R-squared: F-statistic: 44.83 on 1 and 398 DF, p-value: 7.339e-11 0.09898 a. Predict the monthly auto insurance premium for a driver with 10 years of driving experience. b. Compute the standard deviation of errors. c. Using alpha=0.05, test whether B, is different from zero
4. The following output from R presents the results from computing a linear model. In our data example we are interested to study the relationship between students' academic performance api00 with variable enroll which is the number of students in the school. Call: Im (formula = api00 - enrol1, data = d) Residuals: Min 10 Median 30 Мах -285.50 -112.55 -6.70 95.06 389.15 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 744.25141 15.93308 46.711 < 2e-16 *** enroll -0.19987 0.02985 -6.695 7.34e-11 *** Signif. codes: 0 '*** ' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' 1 %3D Residual standard error: 135 on 398 degrees of freedom Multiple R-squared: 0.1012, Adjusted R-squared: F-statistic: 44.83 on 1 and 398 DF, p-value: 7.339e-11 0.09898 a. Predict the monthly auto insurance premium for a driver with 10 years of driving experience. b. Compute the standard deviation of errors. c. Using alpha=0.05, test whether B, is different from zero
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter3: Functions And Graphs
Section3.3: Lines
Problem 76E
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Question
Transcribed Image Text:4. The following output from R presents the results from computing a linear model.
In our data example we are interested to study the relationship between students' academic performance
api00 with variable enroll which is the number of students in the school.
Call:
1m (formula = api00 - enroll, data = d)
Residuals:
мin
10 Median
30
Маx
-285.50 -112.55
-6.70
95.06 389.15
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 744.25141
enroll
15.93308 46.711 < 2e-16 ***
-0.19987
0.02985 -6.695 7.34e-11 ***
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1
' 1
Residual standard error: 135 on 398 degrees of freedom
Multiple R-squared: 0.1012, Adjusted R-squared:
F-statistic: 44.83 on 1 and 398 DF, p-value: 7.339e-11
0.09898
a. Predict the monthly auto insurance premium for a driver with 10 years of driving experience.
b. Compute the standard deviation of errors.
c. Using alpha=0.05, test whether B, is different from zero
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