3. Explain how to predict y-values using the equation of a regression line.

5. To predict y-values using the equation of a regression line, what must be true about the correlation coefficient of the variables?

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

7. The y-value of a data point corresponding to x;

a. \hat{y}_i

b. y_i

c. b

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

e. m

f. \bar{y}"

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

9. Slope

a. \hat{y}_i

b. y_i

c. b

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

e. m

f. \bar{y}"

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

12. The point a regression line always passes through

a. \hat{y}_i

b. y_i

c. b

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

e. m

f. \bar{y}"

4. For a set of data and a corresponding regression line, describe all values of x that provide meaningful predictions for y.

1. Interpret the meaning of the coefficient -8.2 in the multiple regression equation y=112.1+0.43x_1-8.2x_2+29.5x_3.

2. Compare the numbers of dependent and independent variables in a multiple regression equation and a single regression equation.

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

3. Cauliflower Yield The equation used to predict the annual cauliflower yield (in pounds

per acre) is y=24,791+4.508x_1-4.723x_2

where x_1 is the number of acres planted and x_2 is the number of acres harvested.(Adapted from United States Department of Agriculture)

a. x_1 = 36,500, x_2 = 36,100

b. x_1 = 38,100, x_2 = 37,800

c. x_1 = 39,000, x_2 = 38,800

d. x_1 = 42,200, x_2 = 42,100"