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Is high school GPA a good predictor of college GPA? Naked Statistics discusses this issue, but let's explore it with some real data. The table 'satgpa' in the openintro package gives information about test scores and GPA in high school and college for students at an unnamed college. Use the data to build a model to predict first year college GPA ("fy_gpa") from high school GPA ("hs_gpa"). a. Make a scatterplot of "fy_gpa" (y-axis) vs "hs_gpa" (x-axis). Which plot is the scatterplot? Graph A Graph B Graph C Graph D A B hos gpa Tyga hogpa C D hѕ gpa hs_gpa

b. Does the relationship appear roughly linear? Yes, there is no obvious curvature in the graph Yes, all the points fall on a straight line. No, there are gaps in the graph along the x-axis. No, all the points do not fall on a straight line. c. Do the data provide strong evidence that high school GPA and first-year college GPA are associated? State the nuit and alternative hypotheses, report the p-value, and state your conclusion. 1. The hypotheses are: Ho: 81 = 0 HO:* = 0.743 HO:B10 HA: 81 > 0 HA:11 +0.743 HA:B10 HO:21 = 0 HO: B1 = 0.743 HO: I1 = 0 H:1] > 0 HAB: 0.743 HA:110 Hi. The p-value for the test is (round to three decimal places--if that value is 0 then enter 0.): it. The result of this hypothesis test is: About 29.5% of the variation in first-year college GPA is explained by the least squares line. high school GPA is not predictive of first-year college GPA. high school GPA is predictive of first-year college GPA. About 29.5% of the variation in high school GPA is explained by the least squares line. d. Interpret R. The amount of variation in high school GPA that is explained by the least squares line. The total amount of variation in the model. The total unexplained variation in the model the amount of variation in firstyear college GPA that is explained by the least squares line.

About 29.5% of the variation in high school GPA is explained by the least-squares line. d. Interpret R. The amount of variation in high school GPA that is explained by the least squares line. The total amount of variation in the model. The total unexplained variation in the model. the amount of variation in first-year college GPA that is explained by the least squares tine. e. What is the equation of the regression line? GP Acol-0.743 GPA + 0.091 GPA - 0.091 GPA 0.743 GPA-0.743 GP Aco 0.091 GPAM - 0.091 GP Acol +0.743 1. Interpret the slope in the context of the model. For each Select an answer increase in Select an answer 0. Select an answer Select an answer Select an answer 8. Interpret the y-intercept in the context of the model or explain why it should not be interpreted. When the first-year college GPA of a student is the expected value of high school GPA is 0.743. When the high school GPA of a student is the expected value of first-year college GPA is 0.091. When the first-year college GPA of a student is the expected value of high school GPA is 0.091. It does not make sense to interpret the yintercept for this model because no student in the data had a high school GPA anywhere close to 0. Submit Question