Regression and Prediction

Regression and Prediction

A researcher wants to predict graduate-school grade point average (GPA) from GRE verbal scores (which is what the pesky GRE is supposed to do, anyway!). She gathers data on fifty graduate students. Below are the data “high points”:

Independent (predictor) variable X = GRE verbal score
Mean Regression and Prediction.missouri.edu/exec/data/courses2/coursegraphics/2425/X.gif”> = 520
Standard Deviation Regression and Prediction.missouri.edu/exec/data/courses2/coursegraphics/2425/06-8.gif”> = 90
Dependent (criterion) variable Y = GPA
Mean Regression and Prediction.missouri.edu/exec/data/courses2/coursegraphics/2425/06-9.gif”> = 3.30
Standard Deviation Regression and Prediction.missouri.edu/exec/data/courses2/coursegraphics/2425/06-10.gif”> = 0.88
r = .50 (correlation between GRE verbal and GPA)

A. Given a score of 580 on the GRE verbal test, what GPA would you predict?

B. Calculate the standard error for the predicted GPA for 1A.


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