"In Exercises 27 and 28, use the multiple regression equation to predict the y-values for the values of the independent variables.
27. An equation that can be used to predict fuel economy (in miles per gallon) for automobiles is
y=41.3- 0.004x_1 - 0.0049x_2
where x_1 is the engine displacement (in cubic inches) and x_2 is the vehicle weight (in
pounds).
a. x_1 = 305, x_2 = 3750
b. x_1 = 225, x_2 = 3100
c. x_1 = 105, x_2 = 2200
d. x_1 = 185, x_2 = 3000"

"Predicting y-Values In Exercises 3-6, use the multiple regression equation to predict the y-values for the values of the independent variables.

6. Elephant Weight The equation used to predict the weight of an elephant (in kilograms) is

y =- 4016+11.5x_1+7.55x_2+12.5x_3

where x_1 represents the girth of the elephant (in centimeters), x_2 represents the length of the elephant (in centimeters), and x_3 represents the circumference of a footpad (in

centimeters). (Source: Field Trip Earth)

a. x_1 = 421, x_2 = 224, x_3 = 144

b. x_1 = 311, x_2 = 171, x_3 = 102

c. x_1 = 376, x_2 = 226, x_3 = 124

d. x_1 =231, x_2 = 135, x_3 = 86"

"Predicting y-Values In Exercises 3-6, use the multiple regression equation to predict the y-values for the values of the independent variables.

5. Black Cherry Tree Volume The volume (in cubic feet) of a black cherry tree can be modeled by the equation

y =- 52.2+0.3x_1 +4.5x_2

where x_1 is the tree's height (in feet) and x_2 is the tree's diameter (in inches). (Source: Journal of the Royal Statistical Society)

a. x_1 = 70, x_2 = 8.6

b. x_1 = 65, x_2 = 11.0

c. x_1 = 83, x_2 = 17.6

d. x_1 = 87, x_2 = 19.6"

"In Exercises 7-12, match the description in the left column with its symbol(s) in the right column.

10. y-intercept

a. \hat{y}_i

b. y_i

c. b

d. (\bar{x}, \bar{y})

e. m

f. \bar{y}"

Two variables have a bivariate normal distribution. Explain what this means.

"In Exercises 27 and 28, use the multiple regression equation to predict the y-values for the values of the independent variables.

28. Use the regression equation found in Exercise 25.

a. x_1 = 9.0, x_2 = 0.70

b. x_1 = 3.0, x_2 = 0.25

c. x_1 = 8.0, x_2 = 0.60

d. x_1 = 5.2, x_2 = 0.46"

6. Why is it not appropriate to use a regression line to predict y-values for x-values that are not in (or close to) the range of x-values found in the data?

"Constructing and Interpreting a Prediction Interval In Exercises 21-30, construct the indicated prediction interval and interpret the results.

21. Proceeds Construct a 95% prediction interval for the proceeds from initial public offerings in Exercise 11 when the number of offerings is 200."

"Constructing and Interpreting a Prediction Interval In Exercises 21-30, construct the indicated prediction interval and interpret the results.

22. Mean Hourly Wage Construct a 95% prediction interval for the mean hourly wage in Exercise 12 when the median hourly wage is $21.50."