The regression coefficient in the best linear prediction rule is
A)the product of the sum of the deviation scores on the predictor variable,multiplied by the sum of the deviation scores on the criterion variable,divided by the predictor variable's sum of squared deviations from the mean.
B)the sum of the products of the deviation scores,divided by the predictor variable's sum of squared deviations from the mean.
C)the product of the sum of the deviation scores on the predictor variable,multiplied by the sum of the deviation scores on the criterion variable,divided by the criterion variable's sum of squared deviations from the mean.
D)the sum of the product of the deviation scores,divided by the criterion variable's sum of squared deviations from the mean.

Question

Quizplus · Accepted Answer

The regression coefficient in the best linear prediction rule is calculated as the sum of the products of the deviation scores for each pair of values, divided by the sum of squared deviations from the mean for the predictor variable. This describes the slope of the line in linear regression, indicating how much the dependent variable is expected to increase when the independent variable increases by one unit.

The Regression Coefficient in the Best Linear Prediction Rule Is