Question

5).

a.
An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. Click here to view the weight and gas mileage data. (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. y = 1.12345 x + 1.12345 (Round the x coefficient to five decimal places as needed. Round the constant to two decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. Choose the correct answer below and fill in any answer boxes in your choice. (Use the answer from part a to find this answer.) O A. For every pound added to the weight of the car, gas mileage in the city will decrease by 1.11234 mile(s) per gallon, on average. A weightless car will get miles per gallon, on average. OB. For every pound added to the weight of the car, gas mileage in the city will decrease by mile(s) per gallon, on average. It is not appropriate to interpret the y-intercept. OC. A weightless car will get miles per gallon, on average. It is not appropriate to interpret the slope. OD. It is not appropriate to interpret the slope or the y-intercept. (c) A certain gas-powered car weighs 3500 pounds and gets 16 miles per gallon. Is the miles per gallon of this car above average or below average for cars of this weight? Below Above (d) Would it be reasonable to use the least-squares regression line to predict the miles per gallon of a hybrid gas and electric car? Why or why not? O A. Yes, because the absolute value of the correlation coefficient is greater than the critical value for a sample size of n = 11. OB. No, because the absolute value of the correlation coefficient is less than the critical value for a sample size of n = 11. O C. Yes, because the hybrid is partially powered by gas. OD. No, because the hybrid is a different type of car.

Answer 1

No. 1 let, Page weight Y : Miles per gallon x least square regression line Yon is given by , Y = at bysex T² = 2x² - x² byx = Corrx,y) 2 n K a=ý - byn-ze here, n=11 37383 #MASASAS X = Eze 1| x = 3318.454545 210 = 19.090909 9 = EU = n.

(Page No. 2 1 Te2 = { x² x² 12895GGGS 11 (3398.454543 n ..? = 173839.8874 Cou(x,y) Exy - x - - 701916 3398.454545* 19.090909 Cov(x,y)= - 10 69.041005 byx= = Covcx,r) -1069. 041005 173839.8874 byroosts bix= -0.00615 1.0= 19.090909 - (-0.00GIS) 3398.454545 40.00 O = 39.9914 ġ = 40.00+ (-0.00615) OR 1-0.00 GIS +140.00