Probability and Statistics for Engineering and the Sciences
9th Edition
ISBN: 9781305251809
Author: Jay L. Devore
Publisher: Cengage Learning
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Chapter 13.4, Problem 40E
The article cited in Exercise 49 of Chapter 7 gave summary information on a regression in which the dependent variable was power output (W) in a simulated 200-m race and the predictors were x1 = arm girth (cm), x2 = excess post-exercise oxygen consumption (ml/kg), and x3 = immediate posttest lactate (mmol/L). The estimated regression equation was reported as
y = −408.20 + 14.06x1 + 0.76x2 − 3.64x3 (n = 11, R2 = 0.91)
- a. Carry out the model utility test using a significance level of .01. [Note: All three predictors were judged to be important.]
- b. Interpret the estimate 14.06.
- c. Predict power output when arm girth is 36 cm, excess oxygen consumption is 120 ml/kg, and lactate is 10.0.
- d. Calculate a point estimate for true average power output when values of the predictors are as given in (c).
- e. Obtain a point estimate for the true average change in power output associated with a 1 mmol/L increase in lactate while arm girth and oxygen consumption remain fixed.
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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 x₁ = distance traveled (miles) and x₂ = the number of deliveries made. Suppose that the model equation is
Y = -0.800+ 0.060x₁ +0.900x₂ + e
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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 average change in travel time associated with a one-mile (i.e.…
Four pairs of data yield r= 0.942 and regression equation y=3x.Also, y= 12.75. What is the best predicted value of y for x= 2.9?
Chapter 13 Solutions
Probability and Statistics for Engineering and the Sciences
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