Applied Statistics in Business and Economics
5th Edition
ISBN: 9780077837303
Author: David Doane, Lori Seward Senior Instructor of Operations Management
Publisher: McGraw-Hill Education
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Chapter 12.6, Problem 30SE
Instructions for exercises 12.29–12.31: (a) Use Excel’s Data Analysis > Regression (or MegaStat or Minitab) to obtain regression estimates. (b) Interpret the 95 percent confidence interval for the slope. Does it contain zero? (c) Interpret the t test for the slope and its p-value. (d) Interpret the F statistic. (e) Verify that the p-value for F is the same as for the slope’s t statistic, and show that t2 = F. (f) Describe the fit of the regression.
12.30 Annual Percent Return on Mutual Funds (n = 17)
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City Fuel Consumption: Finding the Best Multiple Regression Equation. In Exercises 9–12, refer to the accompanying table, which was obtained using the data from 21 cars listed in Data Set 20 “Car Measurements” in Appendix B. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi /gal).
A Honda Civic weighs 2740 lb, it has an engine displacement of 1.8 L, and its highway fuel consumption is 36 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate?
City Fuel Consumption: Finding the Best Multiple Regression Equation. In Exercises 9–12, refer to the accompanying table, which was obtained using the data from 21 cars listed in Data Set 20 “Car Measurements” in Appendix B. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi /gal).
Which regression equation is best for predicting city fuel consumption? Why?
Chapter 12 Solutions
Applied Statistics in Business and Economics
Ch. 12.1 - Prob. 1SECh. 12.1 - Prob. 2SECh. 12.1 - Prob. 3SECh. 12.1 - Prob. 4SECh. 12.1 - Prob. 5SECh. 12.1 - Prob. 6SECh. 12.2 - (a) Interpret the slope of the fitted regression...Ch. 12.2 - (a) Interpret the slope of the fitted regression...Ch. 12.2 - Prob. 9SECh. 12.2 - (a) Interpret the slope of the fitted regression...
Ch. 12.2 - (a) Interpret the slope of the fitted regression...Ch. 12.3 - Prob. 12SECh. 12.3 - Prob. 13SECh. 12.3 - The regression equation Credits = 15.4 .07 Work...Ch. 12.3 - Below are fitted regressions for Y = asking price...Ch. 12.3 - Refer back to the regression equation in exercise...Ch. 12.3 - Refer back to the regression equation in exercise...Ch. 12.4 - Instructions for exercises 12.18 and 12.19: (a)...Ch. 12.4 - Instructions for exercises 12.18 and 12.19: (a)...Ch. 12.4 - Instructions for exercises 12.2012.22: (a) Use...Ch. 12.4 - Instructions for exercises 12.2012.22: (a) Use...Ch. 12.4 - Instructions for exercises 12.2012.22: (a) Use...Ch. 12.5 - Instructions for exercises 12.23 and 12.24: (a)...Ch. 12.5 - Prob. 24SECh. 12.5 - A regression was performed using data on 32 NFL...Ch. 12.5 - A regression was performed using data on 16...Ch. 12.6 - Prob. 27SECh. 12.6 - Prob. 28SECh. 12.6 - Instructions for exercises 12.2912.31: (a) Use...Ch. 12.6 - Instructions for exercises 12.2912.31: (a) Use...Ch. 12.6 - Instructions for exercises 12.2912.31: (a) Use...Ch. 12.7 - Refer to the Weekly Earnings data set below. (a)...Ch. 12.7 - Prob. 33SECh. 12.8 - Prob. 34SECh. 12.8 - Prob. 35SECh. 12.9 - An estimated regression for a random sample of...Ch. 12.9 - An estimated regression for a random sample of...Ch. 12.9 - Prob. 38SECh. 12.9 - Prob. 39SECh. 12 - (a) How does correlation analysis differ from...Ch. 12 - (a) What is a simple regression model? (b) State...Ch. 12 - (a) Explain how you fit a regression to an Excel...Ch. 12 - (a) Explain the logic of the ordinary least...Ch. 12 - (a) Why cant we use the sum of the residuals to...Ch. 12 - Prob. 6CRCh. 12 - Prob. 7CRCh. 12 - Prob. 8CRCh. 12 - Prob. 9CRCh. 12 - Prob. 10CRCh. 12 - Prob. 11CRCh. 12 - Prob. 12CRCh. 12 - (a) What is heteroscedasticity? Identify its two...Ch. 12 - (a) What is autocorrelation? Identify two main...Ch. 12 - Prob. 15CRCh. 12 - Prob. 16CRCh. 12 - (a) What is a log transform? (b) What are its...Ch. 12 - Prob. 40CECh. 12 - Prob. 41CECh. 12 - Prob. 42CECh. 12 - Prob. 43CECh. 12 - Prob. 44CECh. 12 - Prob. 45CECh. 12 - Prob. 46CECh. 12 - Prob. 47CECh. 12 - Prob. 48CECh. 12 - Prob. 49CECh. 12 - Prob. 50CECh. 12 - Prob. 51CECh. 12 - Prob. 52CECh. 12 - Prob. 53CECh. 12 - Prob. 54CECh. 12 - Prob. 55CECh. 12 - Prob. 56CECh. 12 - Prob. 57CECh. 12 - Prob. 58CECh. 12 - Prob. 59CECh. 12 - In the following regression, X = weekly pay, Y =...Ch. 12 - Prob. 61CECh. 12 - In the following regression, X = total assets (...Ch. 12 - Prob. 63CECh. 12 - Below are percentages for annual sales growth and...Ch. 12 - Prob. 65CECh. 12 - Prob. 66CECh. 12 - Prob. 67CECh. 12 - Simple regression was employed to establish the...Ch. 12 - Prob. 69CECh. 12 - Prob. 70CECh. 12 - Prob. 71CECh. 12 - Below are revenue and profit (both in billions)...Ch. 12 - Below are fitted regressions based on used vehicle...Ch. 12 - Below are results of a regression of Y = average...
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- City Fuel Consumption: Finding the Best Multiple Regression Equation. In Exercises 9–12, refer to the accompanying table, which was obtained using the data from 21 cars listed in Data Set 20 “Car Measurements” in Appendix B. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi /gal). If exactly two predictor (x) variables are to be used to predict the city fuel consumption, which two variables should be chosen? Why?arrow_forwardRegression and Predictions. Exercises 13–28 use the same data sets as Exercises 13–28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493. Internet and Nobel Laureates Find the best predicted Nobel Laureate rate for Japan, which has 79.1 Internet users per 100 people. How does it compare to Japan’s Nobel Laureate rate of 1.5 per 10 million people?arrow_forwardRegression and Predictions. Exercises 13–28 use the same data sets as Exercises 13–28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493. Crickets and Temperature Find the best predicted temperature at a time when a cricket chirps 3000 times in 1 minute. What is wrong with this predicted temperature?arrow_forward
- Regression and Predictions. Exercises 13–28 use the same data sets as Exercises 13–28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493. CPI and the Subway Use the CPI/subway fare data from the preceding exercise and find the best predicted subway fare for a time when the CPI reaches 500. What is wrong with this prediction?arrow_forwardQ1) Interpret the following regression line y = 10.50 – 0.18xarrow_forwardRegression and Predictions. Exercises 13–28 use the same data sets as Exercises 13–28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493. Tips Using the bill/tip data, find the best predicted tip amount for a dinner bill of $100. What tipping rule does the regression equation suggest?arrow_forward
- 10) A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-0.767 b=31.009 r2=0.609961 r=-0.781 Use this to predict the number of situps a person who watches 7.5 hours of TV can do (to one decimal place)arrow_forwardUsing the data below: xi 3 15 yi 17 15 11 14 ) Find the regression line (show your manual calculation, and do not use Excel (i) compute the standard error of the estimate (iii) calculate the estimated standard deviation of b1 and (iv) use the t-test to test if there is a linear relationship exist between x and y. Use a = .05. Do NOT use EXCEL Oa) () y=6+2.03x (i) 10.033 (iii) 0.0043 (iv) There is a linear relationship exist between x and y. O b) (i) y=4+2.03x 的 1.033 (iii) 0.443 (iv) There is a no linear relationship exist between x andy. c) None of the answers are correctarrow_forwardWhich of the figures shows a violation of regression model assumption? 1 2 3 4arrow_forward
- Q: The dataset posted below lists a sample of months and the advertising budget (in hundreds of dollars) for TV, radio and newspaper advertisements. Also included is whether a coupon was published for that month and the resulting sales (in thousands of dollars). a) Develop a multiple regression model predicting the sales based off the four predictor variables: TV, radio, and newspaper advertising budget and whether a coupon is used. Recode Coupon as 0 = No and 1 = Yes. Report the estimated regression equation (Solve in Excel) TV ($100) radio ($100) newspaper ($100) Coupon sales ($1000) 0.7 39.6 8.7 No 1.6 230.1 37.8 69.2 No 22.1 4.1 11.6 5.7 Yes 3.2 44.5 39.3 45.1 No 10.4 250.9 36.5 72.3 No 22.2 8.6 2.1 1 No 4.8 17.2 45.9 69.3 Yes 9.3 104.6 5.7 34.4 No 10.4 216.8 43.9 27.2 Yes 22.3 5.4 29.9 9.4 No 5.3 69 9.3 0.9 No 9.3 70.6 16 40.8 No 10.5 151.5 41.3 58.5 No 18.5 195.4 47.7 52.9 Yes 22.4 13.1 0.4 25.6 Yes 5.3 76.4 0.8…arrow_forwardh) Perform the test using a regression test (using a regression model with dummy variable).arrow_forwardFind the new data point (x,y) in which x=2 from the data points (1.3) and (4.12)arrow_forward
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