1. Bob is interested in examining the relationship between the number of bedrooms in a home and its selling price. After downloading a valid data set from the internet, he calculates the correlation.

The correlation value he calculates is only 0.05. What does Bob conclude?

A. Bob continues his research because even though there is no linear relationship here, there could be a different relationship.

B. Bob gives up on his research because r = .05 means there is no relationship of any kind between bedrooms and selling price.

2. Suppose the correlation between

X =price of a gallon of gasoline

and

Y = price of a gallon of milk

is r = .30

Should we go on and try to make predictions for milk prices using gasoline prices using a straight line?

True

False

3. Your boss gives you the following regression equation.

X = square feet and Y = selling price

Selling price = $5,240 + $33.80 (Number of Square Feet).

Does it make sense to interpret the Y-intercept for this equation?

True

False

4. Data was collected on amount of rainfall (inches) and amount of corn produced (bushels per acre) for a number of years in Kansas. The output is shown below. Assume the scatter plot looks good. What are the units of slope in this situation?

Predictor	Coef	SE Coef	T	P
Constant	89.543	6.703	13.36	0.000
Rainfall	0.12800	0.01375	9.31	0.000
Correlation of Rainfall and Corn = 0.608

a. bushels per acre

b. inches per bushel

c. slope has no units

d. bushels per inch

5. Data was collected on amount of rainfall (inches) and amount of corn produced (bushels per acre) for a number of years in Kansas. The output is shown below. Assume the scatter plot looks good. Which of the following statements is true?

Predictor	Coef	SE Coef	T	P
Constant	89.543	6.703	13.36	0.000
Rainfall	0.12800	0.01375	9.31	0.000
Correlation of Rainfall and Corn = 0.60

a. None of these choices is correct.

b. 60% of the variability in Bushels per acre is due to Inches of rainfall

c. 36% of the variability in Bushels per acre is due to Inches of Rainfall.