test 3 spring

# test 3 spring - 1. Which of the following correlation...

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1. Which of the following correlation coefficients represents the strongest relationship? a. 1.07 b. 0.78 c. -0.86 d. 0.14 e. -0.08 QUESTIONS 2-4: The PJH&D Company is in the process of deciding whether to purchase a maintenance contract for its new word-processing system. They feel that maintenance expense should be related to usage and have collected information on x = weekly usage (hours) and y = annual maintenance expense (\$100s). The data set yielded a linear scatterplot and the following statistics: 10, 25.3, 34.65, 10.25, 10.56, 0.9253 x y n x y s s r = = = = = = 2. Which of the following represents the estimated y-intercept ? a) 16.907 b) 0.953 c) 0.701 d) 10.539 e) None of the above. 3. How much of the variation in annual maintenance expense is accounted for by the least squares regression equation? a) 0.8562 b) 0.9253 c) 0.6810 d) 0.0754 e) None of the above. 4. A linear model fit to predict weekly Sales of frozen pizza (in pounds) from the average price (\$/unit) charged by a sample of stores in the Atlanta area during 39 recent weeks is: · Sales 141,865.53 24,369.49Price = - . If the sales for a price of \$3.50 turned out to be 60,000 pounds, what would the residual be? a) 3427.68 b) 56572.32 c) 25479.94 d) 4520.06 1

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5. At each setting of the independent variables, which of the following are regression model assumptions? a) The errors are dependent. b) The errors have the same variance. c) The errors are normally distributed. d) Both (b) and (c). e) All of the above. QUESTIONS 6-7: Much attention has been paid to the challenges faced by the airline industry. Patterns in customer demand are an important variable to watch. The scatterplot and residual plots below shows the number of passengers departing from Oakland, CA airport month by month since 1990. Simple linear regression results: Independent Variable: Years Since 1990 Passengers = 282585.16 + 59704.3 Years Since 1990 Sample size: 113 R-sq = 0.7112589 Estimate of error standard deviation: 104330.93 6. Interpret the two plots above. a) The scatterplot is approximately linear but the residual plot is not. This reveals a problem with using linear regression.
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## This note was uploaded on 06/05/2011 for the course STAT 3000 taught by Professor Staff during the Spring '08 term at UGA.

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test 3 spring - 1. Which of the following correlation...

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