A linear regression analysis is run on Mr. Chamberlains's physical-fitness course, the x-values are the number of push-ups a student can do and the y-values are the number of sit-ups the student can do. It is determined by a hypothesis test there there is lack of fit but the regression equation is useful for predicting. Which of the following is the best explanation of these results?
A) The analysis needs to be re-run adding the error into the regression sum of squares.
B) Number of push-ups a student does does not accurately predict the number of sit-ups a student can do.
C) Lack of fit means there isn't enough data in the sample to get an accurate regression equation but the equation could help to make some predictions.
D) There may be a better model than linear but a linear model could be used to make predictions.
Dear Student, Please find solution herewith:... View the full answer