DIRECTIONS: Show your work or explain how you arrived at your conclusion whenever appropriate.

Marvin is a safety inspector at a large industrial plant that has eight divisions that do the same type of work. He recorded the number of work-hours devoted to safety training and the number of work-hours lost due to industry-related accidents for each of the divisions last quarter. This data is in the table below.

Number of work-hours in safety Training	9	21	27	42	50	55	41	35
Number of work-hours lost due to accidents	73	45	68	55	35	10	12	32

Marvin used this data to predict the number of work-hours lost due to accidents based upon the number of work-hours spent in safety training using linear regression analysis. His StatCrunch output is given below.

Marvin's output:

Correlation betweenNo. of Hours Safety Training andNo. of Hours Lost Accidentsis: -0.748 (P-value = 0.033).

Simple linear regression results: Dependent Variable:No. of Hours Lost Accidents Independent Variable:No. of Hours Safety Training No. of Hours Lost Accidents = 81.438 - 1.148(No. of Hours Safety Training)

1. State the direction of the relationship between the two variables and interpret its meaning in the context of this problem.

1. Explain whether or not Marvin's use of regression analysis to produce the equation of the "line of best fit" was appropriate in this situation.

1. What would Marvin predict to be the number of work-hours lost due to accidents if 35 hours are spent on safety training? Show how you arrived at your answer and interpret its meaning in the context of this problem.

1. Marvin's supervisor guessed that if 52 hours were spent on safety training then the number of work-hours lost due to accidents would be 20 hours. Explain whether or not this guess was reasonable.

1. Marvin's brother Michael supervises cashiers at Wegmans Supermarkets. What should Michael predict to be the number of work-hours lost by a cashier at Wegmans due to accidents when that cashier has had 20 hours of safety training?