A guidance counselor at a local high school is interested in determining what, i
ID: 3229235 • Letter: A
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 (290/300)x100 = 96.7.
Interpret the value of R2 (i.e. "R Square" in the output above) in the context of the problem. Read carefully!
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 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 3.2% of college GPAs will be able to be perfectly estimated using the variability in the fitted regression line.
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.
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 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 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 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 3.2% of college GPAs will be able to be perfectly estimated using the variability in the fitted regression line.
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.
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 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.
Explanation / Answer
About 82% or higher of the variability in college GPAs can be "explained," or accounted for, by the regression fit between college GPA and high school percentile rank.
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