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 0 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 y=1.09200.0015x. This line is shown in the scatter plot below.

A is greater than or less than.

B is increase or decrease.

Standardized Grade point test score, x average, y 860 2.22 1390 3.14 1490 3.11 930 2.15 X 1280 3.11 X 1240 3.29 800 2.39 Grade point average, y 1000 3.26 1190 X 2.90 X X X 1350 3.53 X 890 2.57 1090 2.32 800 900 1000 1100 1200 1300 1400 1500 1050 2.84 1520 3.36 Standardized test score, x 1000 2.43 Send data to calculator V Send data to Excel Based on the sample data and the regression line, complete the following. (a) For these data, standardized test scores that are less than the mean of the standardized test scores tend to be paired with X 5 grade point averages that are (Choose one) |the mean of the grade point averages. (b) According to the regression equation, for an increase of one point in standardized test score, there is a corresponding Choose one) V of 0.0015 points in grade point average. (c) From the regression equation, what is the predicted grade point average when the standardized test score is 1090? (Round your answer to at least two decimal places.) (d) From the regression equation, what is the predicted grade point average when the standardized test score is 1290 ? (Round your answer to at least two decimal places.)