A guidance counselor at a local high school is interested in determining what, i

Question

A guidance counselor at a local high school is interested in determining what, if any, linear relationship there is between high school percentile ranks and college GPAs. A student's percentile rank is calculated by determining the percentage of all students in the graduating class with a final high school GPA at or below his or hers. For example, a student graduating 10th in a class of 300 would have a percentile rank (to one decimal place) of (290300) times 100 = 96.7. Interpret the value of R^2 in the context of the problem. About 43% of the variability in college GPAs can be "explained, " or accounted for by the regression fit between college GPA and high school percentile rank. About 18% of the variability in college GPAs can be "explained, " or accounted for by the regression fit between college GPA and high school percentile rank. About 82% of the variability in college GPAs can be "explained, " or accounted for by the regression fit between college GPA and high school percentile rank. About 32% of college GPAs will be able to be perfectly estimated using the variability in the fitted regression line. About 57% of the variability in college GPAs can be explained, or accounted for by the regression fit between college GPA and high school percentile rank. About 18% of the variability in high school rank can be "explained, " or accounted for by the regression fit between college GPA and the y-intercept.

Explanation / Answer

About 18% of the variability in college GPAs can be explained or accounted for by the regression fit between college GPA and the high school percentile rank .

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