A popular, nationwide standardized test taken by high-school juniors and Españseniors may or may not measure academic potential, but we cannonetheless attempt to predict performance in college from performanceon this test.We have chosen a random sample of students just finishing their firstyear of college, and for each student we've recorded her score on thisstandardized test (from 400 to 1600) and her grade point average (from oto 4) for her first year in college. The data are shown below, with xdenoting the score on the standardized test andy denoting the first-yearcollege grade point average. The least-squares regression line for thesedata is 1.1239+0.0015x. This line is shown in the scatter plot below.Standardizedtest score, .x136011007901280890Grade pointaverage, y3.772.282.352.912.642.223.183.342.363.303.022.8886014001510100012501060121010209401510Send data to calculator ✓ Send data to Excel3.072.293.01Grade point average,34-324900 1000 1100 1200 1300 1400 1500Standardized test score, xBased on the sample data and the regression line, complete thefollowing.(a) For these data, grade point averages that aregreater than the mean of the grade point averagestend to be paired with standardized test scoresthat are (Choose one) the mean of the standardizedtest scores.(b) According to the regression equation, for anincrease of one point in standardized test score,there is a corresponding increase of how manypoints in grade point average? (Do not round youranswer.)(c) What was the observed grade point average whenthe standardized test score was 1060?0(d) From the regression equation, what is thepredicted grade point average when thestandardized test score is 1060? (Round youranswer to at least two decimal places.)0X5 A popular, nationwide standardized test taken by high-school juniors andseniors may or may not measure academic potential, but we cannonetheless attempt to predict performance in college from performanceon this test.We have chosen a random sample of students just finishing their firstyear of college, and for each student we've recorded her score on thisstandardized test (from 400 to 1600) and her grade point average (from oto 4) for her first year in college. The data are shown below, with xdenoting the score on the standardized test and y denoting the first-yearcollege grade point average. The least-squares regression line for thesedata is 1.1239+0.0015x. This line is shown in the scatter plot below.Standardizedtest score, .xGrade pointaverage, y3.772.282.352.912.642.223.183.342.363.303.022.883.072.293.0113601100790128089086014001510100012501060121010209401510Send data to calculator ✓ (Send data to ExcelGrade point average, y3.4-3.243428-xdo 900 1000 1100 1200 1300 1400 1500Standardized test score, xBased on the sample data and the regression line, complete thefollowing.(a) For these data, grade point averages that aregreater than the mean of the grade point averagestend to be paired with standardized test scoresthat are (Choose one) the mean of the standardizedtest scor greater thanless than(b) According to the regression equation, for anincrease of one point in standardized test score,there is a corresponding increase of how manypoints in grade point average? (Do not round youranswer.)0(c) What was the observed grade point average whenthe standardized test score was 1060?(d) From the regression equation, what is thepredicted grade point average when thestandardized test score is 1060? (Round youranswer to at least two decimal places.)0XE

a) In this case, the independent variable is standardized test score (x) and the dependent variable…

A popular, nationwide standardized test taken by high-school juniors and seniors may or may not measure academic potential, but we can nonetheless attempt to predict performance in college from performance on this test. We have chosen a random sample of students just finishing their first year of college, and for each student we've recorded her score on this standardized test (from 400 to 1600) and her grade point average (from o to 4) for her first year in college. The data are shown below, with x denoting the score on the standardized test and y denoting the first-year college grade point average. The least-squares regression line for these data is 1.1239+0.0015x. This line is shown in the scatter plot below. Standardized Grade point test score, xaverage, y 1360 1100 790 1280 890 860 1400 3.77 2.28 2.35 2.91 2.64 2.22 3.18 3.34 2.36 1510 1000 1250 1060 1210 2.88 1020 3.07 940 2.29 1510 3.01 Send data to calculator v Send data to Excel) 3.30 3.02 Grade point average, y 364 344 324 34 284 de 13te 1300 1400 1500 Standardized test score, x Based on the sample data and the regression line, complete the following. (a) For these data, grade point averages that are greater than the mean of the grade point averages tend to be paired with standardized test scores that are (Choose one) the mean of the standardized test scores. (b) According to the regression equation, for an increase of one point in standardized test score, there is a corresponding increase of how many points in grade point average? (Do not round your answer.) 0 (c) What was the observed grade point average when the standardized test score was 1060? 0 (d) From the regression equation, what is the predicted grade point average when the standardized test score is 1060? (Round your answer to at least two decimal places.) X

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P