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Business Statistics In Practice 6th Edition Bruce Bowerman, Richard O'Connell - Solutions

Perform pairwise comparisons of the treatment means in Exercise 11.12 by using Tukey simultaneous 95 percent confidence intervals. Which brand(s) are most durable? Find a 95 percent confidence interval for each of the treatment means.
Recall from Example 11.3 that (1) 29 randomly selected subjects were exposed to commercials shown in more involving programs, (2) 29 randomly selected subjects were exposed to commercials shown in less involving programs, and (3) 29 randomly selected subjects watched commercials only (note: this is
A marketing organization wishes to study the effects of four sales methods on weekly sales of a product. The organization employs a randomized block design in which three salesman use each sales method. The results obtained are given in Table 11.9. Figure 11.8 gives the Excel output of a randomized
A consumer preference study involving three different bottle designs (A, B, and C) for the jumbo size of a new liquid laundry detergent was carried out using a randomized block experimental design, with supermarkets as blocks. Specifically, four supermarkets were supplied with all three bottle
To compare three brands of computer keyboards, four data entry specialists were randomly selected. Each specialist used all three keyboards to enter the same kind of text material for 10 minutes, and the number of words entered per minute was recorded. The data obtained are given in Table 11.11. If
In an advertisement in a local newspaper, Best Food supermarket attempted to convince consumers that it offered them the lowest total food bill. To do this, Best Food presented the following comparison of the prices of 60 grocery items purchased at three supermarkets-Best Food, Public, and Cash' N
The Coca-Cola Company introduced new Coke in 1985. Within three months of this introduction, negative consumer reaction forced Coca-Cola to reintroduce the original formula of Coke as Coca-Cola classic. Suppose that two years later, in 1987, a marketing research firm in Chicago compared the sales
An experiment is conducted to study the effects of two sales approaches-high-pressure (H) and low-pressure (L)-and to study the effects of two sales pitches (1 and 2) on the weekly sales of a product. The data in Table 11.14 on the next page are obtained by using a completely randomizedTABLE
A study compared three display panels used by air traffic controllers. Each display panel was tested for four different simulated emergency conditions. Twenty-four highly trained air traffic controllers were used in the study. Two controllers were randomly assigned to each display panel-emergency
A telemarketing firm has studied the effects of two factors on the response to its television advertisements. The first factor is the time of day at which the ad is run, while the second is the position of the ad within the hour. The data in Table 11.16, which were obtained by using a completely
A small builder of speculative homes builds three basic house designs and employs two foremen. The builder has used each foreman to build two houses of each design and has obtained the profits given in Table 11.17 (the profits are given in thousands of dollars). Figure 11.16 presents the MINITAB
In the article "Humor in American, British, and German Ads" (Industrial Marketing Management, vol. 22, 1993), L. S. McCullough and R. K. Taylor study humor in trade magazine advertisements. A sample of 665 ads was categorized according to two factors: nationality (American, British, or German) and
A drug company wishes to compare the effects of three different drugs (X, Y, and Z) that are being developed to reduce cholesterol levels. Each drug is administered to six patients at the recommended dosage for six months. At the end of this period the reduction in cholesterol level is recorded for
A sample of 45 auditors was randomly divided into three groups: (l) the 15 auditors in group 1 designed an audit program for accounts receivable and evaluated an audit program for accounts payable designed by somebody else; (2) the 15 auditors in group 2 did the reverse; (3) the 15 auditors in
The loan officers at a large bank can use three different methods for evaluating loan applications. Loan decisions can be based on (1) the applicant's balance sheet (B), (2) examination of key financial ratios (F), or (3) use of a new decision support system (D). In order to compare these three
W. D. Hicks and R. J. Klimoski studied the effects of two factors-degree of attendance choice and prior information-on managers' evaluation of a two-day workshop concerning performance reviews. Degree of attendance choice had two levels: high (little pressure from supervisors to attend) and low
An information systems manager wishes to compare the execution speed (in seconds) for a standard statistical software package using three different compilers. The manager tests each compiler using three different computer models, and the data in Table 11.20 are obtained. Completely analyze the data
In an article from the Journal of Statistics Education, Robin Lock describes a rich set of interesting data on selected attributes for a sample of 1993-model new cars. These data support a wide range of analyses. Indeed, the analysis possibilities are the subject of Lock's article. Here our
Explain why we conduct pairwise comparisons of treatment means. Discuss.
Consider Example 11.2, and let µB, µM and µr represent the mean monthly sales when using the bottom, middle, and top shelf display heights, respectively. Figure 11.3 gives the MINITAB output of a one-way ANOVA of the bakery sales study data in Table 11.2FIGURE 11.3MINITAB Output of a One-Way
Describe the characteristics that define a multinomial experiment. Discuss.
A wholesaler has recently developed a computerized sales invoicing system. Prior to implementing this system, a manual system was used. The distribution of the number of errors per invoice for the manual system is as follows:After implementation of the computerized system, a random sample of 500
Consider the sample of 65 payment times given in Table 2.4. Use these data to carry out a chi square goodness of fit test to test whether the population of all payment times is normally distributed by doing the following:a. It can be shown that = 18.1077 and that s = 3.9612 for the payment time
Consider the sample of 60 bottle design ratings given in Table l .5. Use these data to carry out a chi-square goodness of fit test to determine whether the population of all bottle design ratings is normally distributed. Use α = .05, and note that = 30.35 and s = 3.1073 for the 60 bottle design
Consider the sample of 100 waiting times given in Table 1.8. Use these data to carry out a chi square goodness of fit test to determine whether the population of all waiting times is normally distributed. Use α = .10, and note that = 5.46 and s = 2.475 for the 100 waiting times.
The table on the next page gives a frequency distribution describing the number of errors found in 30 l,000-line samples of computer code. Suppose that we wish to determine whether the number of errors can be described by a Poisson distribution with mean µ = 4.5. Using the Poisson probability
When performing a chi-square test for independence, explain how the "cell frequencies under the independence assumption" are calculated. For what purpose are these frequencies calculated?
A marketing research firm wishes to study the relationship between wine consumption and whether a person likes to watch professional tennis on television. One hundred randomly selected people are asked whether they drink wine and whether they watch tennis. The following results are obtained:a. For
In recent years major efforts have been made to standardize accounting practices in different countries; this is called harmonization. In an article in Accounting and Business Research, Emmanuel N. Emenyonu and Sidney J. Gray studied the extent to which accounting practices in France, Germany, and
Donald R. Cooper and C. William Emory discuss studying the relationship between on-the-job accidents and smoking. Cooper and Emory describe the study as follows:Suppose a manager implementing a smoke-free workplace policy is interested in whether smoking affects worker accidents. Since the company
Thomas J. Madden, and Neil A. Firtle discuss the relationship between delivery time and computer-assisted ordering. A sample of 40 firms shows that 16 use computer-assisted ordering, while 24 do not. Furthermore, past data are used to categorize each firm's delivery times as below the industry
A television station wishes to study the relationship between viewership of its 11 P.M. news program and viewer age (18 years or less, 19 to 35, 36 to 54, 55 or older). A sample of 250 television viewers in each age group is randomly selected, and the number who watch the station's 11 P.M. news is
A large supermarket conducted a consumer preference study by recording the brand of wheat bread purchased by customers in its stores. The supermarket carries four brands of wheat bread, and the brand preferences of a random sample of 200 purchasers are given in the following table:
An occupant traffic study was carried out to aid in the remodeling of a large building on a university campus. The building has five entrances, and the choice of entrance was recorded for a random sample of 300 persons entering the building. The results obtained are given in the following
In a 1993 article in Accounting and Business Research, Meier, Alam, and Pearson studied auditor lobbying on several proposed U.S. accounting standards that affect banks and savings and loan associations. As part of this study, the authors investigated auditors' positions regarding proposed changes
Donald R. Cooper and C. William Emory discuss a market researcher for an automaker who is studying consumer preferences for styling features of larger sedans. Buyers, who were classified as "first-time" buyers or "repeat" buyers, were asked to express their preference for one of two types of
Again consider the situation of Exercise 12.24. Table 12.10 summarizes auditor positions regarding proposed changes in accounting standards that would decrease client firms' reported earnings. Determine whether the relationship between auditor position and the size of the client firm is the same
Consider the sample of 65 customer satisfaction ratings given in Table 12.12. Carry out a chi-square goodness of fit test of normality for the population of all customer satisfaction ratings. Recall that we previously calculated = 42.95 and s = 2.6424 for the 65 ratings.TABLE 12.12A Sample of 65
A report on the 1995 National Health Risk Behavior Survey, conducted by the Centers for Disease Control and Prevention, can be found at the CDC website [www.cdc.gov: More Data & Statistics: CDC Data & Statistics Resources, Surveys Tab: CDC Surveys, Youth Risk Behavior Surveillance System
The shares of the U.S. automobile market held in 1990 by General Motors. Japanese manufacturers. Ford, Chrysler, and other manufacturers were, respectively, 36%, 26%, 21%, 9%, and 8%. Suppose that a new survey of 1,000 new-car buyers shows the following purchase frequencies:a. Show that it is
Last rating period, the percentages of viewers watching several channels between 11 P.M. and 11:30 P.M. in a major TV market were as follows:Suppose that in the current rating period, a survey of 2.000 viewers gives the following frequencies:a. Show that it is appropriate to carry out a chi-square
In the Journal of Marketing Research (November 1996), Gupta studied the extent to which the purchase behavior of scanner panels is representative of overall brand preferences. A scanner panel is a sample of households whose purchase data are recorded when a magnetic identification card is presented
The purchase frequencies for six different brands of videotape are observed at a video store over one month:a. Carry out a test of homogeneity for these data with Î± = .025.b. Interpret the result of your test.
When a least squares line is tit to the 8 observations in the fuel consumption data, we obtain SSE = 2.568. Calculate s2 and s.
When a least squares line is fit to the 7 observations in the starting salary data, we obtain SSE = 1.438. Calculate ,s2 and s.
When a least squares line is fit to the 11 observations in the service time data, we obtain SSE = 191.7017. Calculate .s2 and .s.
When a least squares line is fit to the 10 observations in the real estate sales price data, we obtain SSE = 896.8. Calculate .s2 and .S.
Ten sales regions of equal sales potential for a company were randomly selected. The advertising expenditures (in units of $10,000) in these 10 sales regions were purposely set during July of last year at, respectively, 5, 6, 7, 8, 9, 10, 11, 12, 13 and 14. The sales volumes (in units of $10,000)
The Excel and MINITAB outputs of a simple linear regression analysis of the data set for this case (see Exercise 13.3) are given in Figures 13.9 and 13.10. Labeled Excel and MINITAB outputs are on page 535 in Figure 13.8. Use whichever package is taught in your class.
The MINITAB output of a simple linear regression analysis of the data set for this case (see Exercise 13.4) is given in Figure 13.11. Recall that a labeled MINITAB regression output is on page 535.FIGURE 13.11MINITAB Output of a Simple Linear Regression Analysis of the Starting Salary Data
The Excel output of a simple linear regression analysis of the data set for this case (see Exercise 13.5 on pages 527 and 528) is given in Figure 13.12. Recall that a labeled Excel regression output is on page 535.
The MINITAB output of a simple linear regression analysis of the data set for this case (see Exercise 13.6 on page 528) is given in Figure 13.13. Recall that a labeled MINITAB regression output is on page 535.
The Excel output of a simple linear regression analysis of the data set for this case (see Exercise 13.7 on page 529) is given in Figure 13.14. Recall that a labeled Excel regression output is on page 535.
The MINITAB output of a simple linear regression analysis of the data set for this case (see Exercise 13.8 on page 529) is given in Figure 13.15. Recall that a labeled MINITAB regression output is on page 535.
Find and interpret a 95 percent confidence interval for the slope B] of the simple linear regression model describing the sales volume data in Exercise 13.17 (page 533).
In the early 1990s researchers at The Ohio State University studied consumer ratings of six fast-food restaurants: Borden Burger. Hardee's. Burger King. McDonald's. Wendy's, and White Castle. Each of 406 randomly selected individuals gave each restaurant a rating of 1, 2. 3. 4. 5, or 6 on the basis
On the next page we give the average hourly outdoor temperature (x) in a city during a week and the city's natural gas consumption (y) during the week for each of eight weeks (the temperature readings are expressed in degrees Fahrenheit and the natural gas consumptions are expressed inmillions of
The following partial MINITAB regression output for the fuel consumption data relates to predicting the city's fuel consumption (in MMcf of natural gas) in a week that has an average hourly temperature of 40°F.a. Report (as shown on the computer output) a point estimate of and a 95 percent
The following partial MINITAB regression output for the starting salary data relates to predicting the starting salary of a marketing graduate having a grade point average of 3.25.a. Report (as shown on the computer output) a point estimate of and a 95 percent confidence interval for the mean
The following partial Excel add-in (MegaStat) regression output for the service time data relates to predicting service times for 1, 2, 3, 4, 5, 6, and 7 copiers.a. Report (as shown on the computer output) a point estimate of and a 95 percent confidence interval for the mean time to service four
The following partial MINITAB regression output for the Fresh detergent data relates to predicting demand for future sales periods in which the price difference will be. 10 (see New Obs l) and .25 (see New Obs2).a. Report (as shown on the computer output) a point estimate of and a 95 percent
The following partial Excel add-in (MegaStat) regression output for the direct labor cost data relates to predicting direct labor cost when the batch size is 60.a. Report (as shown on the computer output) a point estimate of and a 95 percent confidence interval for the mean direct labor cost of all
The following partial MINITAB regression output for the real estate sales price data relates to predicting the sales price of a home having 2,000 square feet.a. Report (as shown on the MINITAB output) a point estimate of and a 95 percent confidence interval for the mean sales price of all houses
Discuss the meanings of the total variation, the unexplained variation, and the explained variation. Discuss.
Total variation = 25.549; SSE = 2.568; b1 = -.12792
Total variation = 61.380; SSE = 1.438; b1 = 5.7066
The chairman of the marketing department at a large state university undertakes a study to relate starting salary (y) after graduation for marketing majors to grade point average (GPA) in major courses. To do this, records of seven recent marketing graduates are randomly selected, and the data
Total variation = 20.110.5455; SSE = 191.7017; b1, = 24.6022
Total variation = 13.459; SSE = 2.806; b1, = 2.6652
Total variation = 1,025,339.6667; SSE = 746.7624; b1, = 10.1463
Total variation = 7447.5; SSE = 896.8; b1, = 5.7003
Accu-Copiers, Inc., sells and services the Accu-500 copying machine. As part of its standard service contract, the company agrees to perform routine service on this copier. To obtain information about the time it takes to perform routine service, Accu-Copiers has collected data for 11 service
We give MINITAB and Excel outputs of simple linear regression analyses of the data sets related to six previously discussed case studies. Using the appropriate computer output.a. Use the explained variation and the unexplained variation as given on the computer output to calculate (within rounding)
We give MINITAB and Excel outputs of simple linear regression analyses of the data sets related to six previously discussed case studies. Using the appropriate computer output.a. Use the explained variation and the unexplained variation as given on the computer output to calculate (within rounding)
We give MINITAB and Excel outputs of simple linear regression analyses of the data sets related to six previously discussed case studies. Using the appropriate computer output.a. Use the explained variation and the unexplained variation as given on the computer output to calculate (within rounding)
We give MINITAB and Excel outputs of simple linear regression analyses of the data sets related to six previously discussed case studies. Using the appropriate computer output.a. Use the explained variation and the unexplained variation as given on the computer output to calculate (within rounding)
We give MINITAB and Excel outputs of simple linear regression analyses of the data sets related to six previously discussed case studies. Using the appropriate computer output.a. Use the explained variation and the unexplained variation as given on the computer output to calculate (within rounding)
We give MINITAB and Excel outputs of simple linear regression analyses of the data sets related to six previously discussed case studies. Using the appropriate computer output.a. Use the explained variation and the unexplained variation as given on the computer output to calculate (within rounding)
Enterprise Industries produces Fresh, a brand of liquid laundry detergent. In order to study the relationship between price and demand for the large bottle of Fresh, the company has gathered data concerning demand for Fresh over the last 30 sales periods (each sales period is four weeks). Here, for
Figure 13.28(a) gives the residuals from the simple linear regression model describing the service time data in Exercise 13.5.a. In this exercise we construct a normal plot of the residuals from the simple linear regression model. To construct this plot, we must first arrange the residuals in order
A simple linear regression model is employed to analyze the 24 monthly observations given in Table 13.4. Residuals are computed and are plotted versus time. The resulting residual plot is shown in Figure 13.30. Discuss why the residual plot suggests the existence of positive autocorrelation.TABLE
Consider the following data concerning the demand (y) and price (x) of a consumer product.a. Plot y versus x. Does it seem reasonable to use the simple linear regression model to relate y to x?b. Calculate the least squares point estimates of the parameters in the simple linear regression model.c.
In an article in Public Roads (1983), Bissell, Pilkington, Mason, and Woods study bridge safety (measured in accident rates per 100 million vehicles) and the difference between the width of the bridge and the width of the roadway approach (road plus shoulder):The MINITAB output of a simple linear
In an article in the Journal of Accounting Research, Ben/.ion Barlev and Haim Levy consider relating accounting rates on stocks and market returns. Fifty-four companies were selected. For each company the authors recorded values of A. the mean yearly accounting rate for the period 1959 to 1974, and
In analyzing the stock market, we sometimes use the model y = Î²0 + B1x + Îµ to relate y, the rate of return on a particular stock, to x, the rate of return on the overall stock market. When using the preceding model, we can interpret Î²1 to be the percentage point change in the mean (or
The U.S. News & World Report website provides rankings of the best colleges and universities in the United States. The free version of Best Colleges gives information such as number of students enrolled, tuition rates, and so forth. Among the data provided are the percentage acceptance rate (at
A real estate agency collects data concerning y = the sales price of a house (in thousands of dollars), and x = the home size (in hundreds of square feet). The data are given in the table below. The MINITAB output from fitting a least squares regression line to the data is on the next page.a. By
What lour assumptions do we make about the simple linear regression model?
Model: y = Î²0 + Î²1x1 + Î²2x2 + Îµ Sample size: n = 8
Model: y = Î²0 + Î²1x1 + Î²2x2 + Îµ Sample size: n = 10
Model: y = Î²0 + Î²1x1 + Î²2x2 + Î²3x3 +Îµ Sample size: n = 30
Model: y = Î²0 + Î²1x1 + Î²2x2 + Î²3x3 + Îµ Sample size: n = 16
Use the MINITAB output in Figure 14.9(a) to do (1) through (6) for each of Î²0, Î²1 and Î²3.FIGURE 14.9(a)t Statistics and p-Values for Four Case Studies(a) MINITAB output for the fuel consumption case (sample size: n = 8)
Use the MINITAB output in Figure 14.9(b) to do (1) through (6) for each of Î²0, Î²1 and Î²2.Figure 14.9(b)(b) MINITAB output for the real estate sales price case (sample size: n = 10)
Use the Excel output in Figure 14.9(c) to do (1) through (6) for each of Î²0, Î²1, Î²2. and Î²3.Figure 14.9(c)(c) Excel output for the Fresh detergent case (sample size: n = 30)
Use the Excel output in Figure 14.9(d) to do (1) through (6) for each of Î²0, Î²1, Î²2. and Î²3Figure 14.9(d)(d) Excel output for the hospital labor needs case (sample size: n = 16)
What is the difference between a confidence interval and a prediction interval? Discuss in detail.
What does the distance value measure? How does the distance value affect a confidence or prediction interval? (You must read the optional technical note to answer this question).
The following partial MINITAB regression output for the fuel consumption data relates to predicting the city's fuel consumption (in MMcF of natural gas) in a week that has an average hourly temperature of 40°F and a chill index of l0.a. Report (as shown on the computer output) a point estimate of

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