The best linear prediction rule is the one that has the least error when predict

Question

The best linear prediction rule is the one that has the least error when predicting from the mean Squared error when predicting from the mean, error when predicting using that rule, squared error when predicting using that rule The sum of the squared errors w hen predicting from the mean is called SS proportionate reduction in error. SS proportion of variance accounted for. What is the formula for the proportionate reduction in error? (SS_Error - SS_Total)/SS_Error (SS_Error + SS_Total)/SS_Error (SS_Total - SS_Error)/SS_Total (SS_Total/(SS_Error + SS_Total What does it mean when SS_Total minus SS_Error equals zero? This is the best case-it means there is zero error. This is the worst case-it means the prediction model has reduced zero error. The proportionate reduction in error is 50%. The underlying correlation is negative. When drawing a regression line for a linear prediction rule, the minimum number of predicted points on a graph that must be located is 1. 2. 1 if it is a positively sloped line; 2 if it is a negatively sloped line. 2 if it is a positively sloped line; 1 if it is a negatively sloped line.

Explanation / Answer

Below are the answers for the above multiple choice questions. left side denote the question number and right side that anwere of the perticular questions

1.D

2.C

3.A

4.B

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