A trucking company considered a multiple regression model for relating the dependent variable y = total daily travel time for one of its drivers (hours) to the predictors x1 = distance traveled (miles) and x2 = the number of deliveries made. Suppose that the model equation is Y = -0.800 + 0.060x, + 0.900x2 + E

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A trucking company considered a multiple regression model for relating the
dependent variable y = total daily travel time for one of its drivers (hours) to
the predictors x, = distance traveled (miles) and x, = the number of
deliveries made. Suppose that the model equation is
Y = -0.800 + 0.060x, + 0.900x2 + €
(a) What is the mean value of travel time when distance traveled is 50
miles and four deliveries are made?
5.8
v hr
(b) How would you interpret ß1
= 0.060, the coefficient of the predictor
X1?
o When the number of deliveries is held fixed, the average change
in travel time associated with a one-mile (i.e. one unit) increase in
distance traveled is 0.060 hours.
O The total daily travel time increases by 0.060 hours when the
distance traveled increases by 1.
O When the number of deliveries is constant, the average change in
travel time associated with a ten-mile (i.e. one unit) increase in
distance traveled is 0.060 hours.
O The average change in travel time associated with a one-mile (i.e.
one unit) increase in distance traveled is 0.060 hours.
What is the interpretation of ß, = 0.900?
O The average change in travel time associated with one extra
delivery is 0.900 hours.
o When the distance traveled is held fixed, the average change in
travel time associated with one extra delivery is 0.900 hours.
O When the distance traveled is constant, the change in travel time
associated with one delivery is 0.900 hours.
O The total daily travel time increases by 0.900 hours with one extra
delivery.
(c) If o = 0.5 hour, what is the probability that travel time will be at
most 6 hours when four deliveries are made and the distance traveled is
50 miles? (Round your answer to four decimal places.)
Transcribed Image Text:A trucking company considered a multiple regression model for relating the dependent variable y = total daily travel time for one of its drivers (hours) to the predictors x, = distance traveled (miles) and x, = the number of deliveries made. Suppose that the model equation is Y = -0.800 + 0.060x, + 0.900x2 + € (a) What is the mean value of travel time when distance traveled is 50 miles and four deliveries are made? 5.8 v hr (b) How would you interpret ß1 = 0.060, the coefficient of the predictor X1? o When the number of deliveries is held fixed, the average change in travel time associated with a one-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The total daily travel time increases by 0.060 hours when the distance traveled increases by 1. O When the number of deliveries is constant, the average change in travel time associated with a ten-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The average change in travel time associated with a one-mile (i.e. one unit) increase in distance traveled is 0.060 hours. What is the interpretation of ß, = 0.900? O The average change in travel time associated with one extra delivery is 0.900 hours. o When the distance traveled is held fixed, the average change in travel time associated with one extra delivery is 0.900 hours. O When the distance traveled is constant, the change in travel time associated with one delivery is 0.900 hours. O The total daily travel time increases by 0.900 hours with one extra delivery. (c) If o = 0.5 hour, what is the probability that travel time will be at most 6 hours when four deliveries are made and the distance traveled is 50 miles? (Round your answer to four decimal places.)
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