Statistical Techniques in Business and Economics
16th Edition
ISBN: 9780077639723
Author: Lind
Publisher: Mcgraw-Hill Course Content Delivery
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Chapter 13, Problem 3SR
a.
To determine
Find the regression equation.
b.
To determine
Interpret the values of a and b.
c.
To determine
Find the sales when $3 million is spent on advertising.
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The statistics computed below use data from a number of recently released movies that includes the
USGross (in $), the Budget ($), the Run Time (minutes), and the average number of stars awarded by
reviewers. The multiple regression equation is shown below. A middle manager at an entertainment company,
upon seeing this analysis, concludes that the longer you make a movie, the less money it will make. He argues
that his company's films should all be cut by 25 minutes to improve their gross. Explain the flaw in his
interpretation of this model.
USGross = -22.9898 +1.13442Budget +24.9724Stars -0.403296Run Time
Choose the correct answer below.
O A. Since the coefficient for Run Time is less than one, making a movie shorter may or may not increase
its gross.
O B. The model says that longer films had larger gross incomes after allowing for Budget and Stars, so
making a movie longer will increase its gross.
O C.
Since the coefficient for Run Time is so small, the studio should cut the films…
The table below shows the average weekly wages (in dollars) for state government employees and federal government employees for 10 years. Construct and interpret
a 95% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $895.
The equation of the regression line is y = 1.454x- 36.984.
Wages (state), x
Wages (federal), y
712
778
789
801
843
878
927
937
939
958
1,014
1,054
1,116
1,146
1,191
1,248
1,270
1,302
1,335
1,401
Construct and interpret a 95% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government
employees is $895. Select the correct choice below and fill in the answer boxes to complete your choice.
(Round to the nearest cent as needed.)
O A. We can be 95% confident that when the average weekly wages of state government employees is $895, the average weekly wages of federal government
employees will be between $
and…
The managing director of a consulting group has the accompanying monthly data on total overhead costs and professional labor hours to bill to clients.
Complete parts a through c.
E Click the icon to view the monthly data.
a. Develop a simple linear regression model between billable hours and overhead costs.
Overhead Costs =
5304.4 + (0.0219) × Billable Hours
+1
(Round the constant to one decimal place as needed. Round the coefficient to four decimal places as needed. Do not include the $ symbol in your answers.)
Monthly overhead costs and billable hours data
Overhead Costs
Billable Hours O
IT
$385,000
3,000
$430,000
4,000
$460,000
5,000
$511,000
6,000
$580,000
7,000
$600,000
8,000
Print
Done
Chapter 13 Solutions
Statistical Techniques in Business and Economics
Ch. 13 - Prob. 1SRCh. 13 - Prob. 1ECh. 13 - Prob. 2ECh. 13 - Bi-lo Appliance Super-Store has outlets in several...Ch. 13 - Prob. 4ECh. 13 - Prob. 5ECh. 13 - The owner of Maumee Ford-Volvo wants to study the...Ch. 13 - Prob. 2SRCh. 13 - Prob. 7ECh. 13 - Prob. 8E
Ch. 13 - Prob. 9ECh. 13 - Prob. 10ECh. 13 - Prob. 11ECh. 13 - Prob. 12ECh. 13 - Prob. 3SRCh. 13 - Prob. 13ECh. 13 - Prob. 14ECh. 13 - Prob. 15ECh. 13 - Prob. 16ECh. 13 - Prob. 17ECh. 13 - Prob. 18ECh. 13 - Prob. 19ECh. 13 - Prob. 20ECh. 13 - Prob. 4SRCh. 13 - Prob. 21ECh. 13 - Prob. 22ECh. 13 - Prob. 23ECh. 13 - Prob. 24ECh. 13 - Prob. 5SRCh. 13 - Prob. 25ECh. 13 - Prob. 26ECh. 13 - Prob. 27ECh. 13 - Prob. 28ECh. 13 - Prob. 29ECh. 13 - Prob. 30ECh. 13 - Prob. 6SRCh. 13 - Prob. 31ECh. 13 - Prob. 32ECh. 13 - Prob. 33ECh. 13 - Refer to Exercise 16. a. Determine the .95...Ch. 13 - Prob. 35ECh. 13 - A regional commuter airline selected a random...Ch. 13 - Prob. 38CECh. 13 - Prob. 39CECh. 13 - Prob. 40CECh. 13 - The table below shows the number of cars (in...Ch. 13 - Prob. 42CECh. 13 - Prob. 43CECh. 13 - Prob. 44CECh. 13 - The manufacturer of Cardio Glide exercise...Ch. 13 - Prob. 46CECh. 13 - Prob. 47CECh. 13 - Prob. 48CECh. 13 - Prob. 49CECh. 13 - Mr. William Profit is studying companies going...Ch. 13 - Prob. 51CECh. 13 - Prob. 52CECh. 13 - Prob. 53CECh. 13 - Prob. 54CECh. 13 - A regression analysis relating the current market...Ch. 13 - Prob. 56CECh. 13 - Prob. 57CECh. 13 - A consumer buying cooperative tested the effective...Ch. 13 - Prob. 59CECh. 13 - Prob. 60CECh. 13 - TravelAir.com samples domestic airline flights to...Ch. 13 - Prob. 62DECh. 13 - Refer to the Baseball 2012 data, which reports...Ch. 13 - Prob. 64DE
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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardLife Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300arrow_forwardXYZ Corporation Stock Prices The following table shows the average stock price, in dollars, of XYZ Corporation in the given month. Month Stock price January 2011 43.71 February 2011 44.22 March 2011 44.44 April 2011 45.17 May 2011 45.97 a. Find the equation of the regression line. Round the regression coefficients to three decimal places. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict the stock price to be in January 2012? January 2013?arrow_forward
- Cable TV The following table shows the number C. in millions, of basic subscribers to cable TV in the indicated year These data are from the Statistical Abstract of the United States. Year 1975 1980 1985 1990 1995 2000 C 9.8 17.5 35.4 50.5 60.6 60.6 a. Use regression to find a logistic model for these data. b. By what annual percentage would you expect the number of cable subscribers to grow in the absence of limiting factors? c. The estimated number of subscribers in 2005 was 65.3million. What light does this shed on the model you found in part a?arrow_forwardOlympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forwardCellular Phone Subscribers The table shows the numbers of cellular phone subscribers y in millions in the United States from 2008 through 2013. Source: CTIA- The Wireless Association Year200820092010201120122013Number,y270286296316326336 (a) Find the least squares regression line for the data. Let x represent the year, with x=8 corresponding to 2008. (b) Use the linear regression capabilities of a graphing utility to find a linear model for the data. How does this model compare with the model obtained in part a? (c) Use the linear model to create a table of estimated values for y. Compare the estimated values with the actual data.arrow_forward
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