SOLUTIONS MANUAL FOR Analyzing Data and Making Decisions Statistics for Business Microsoft Excel 201

Contents Part I: Introduction Chapter 1: Using Data to Make Better Decisions................................................................1

Part II: Descriptive Statistics Chapter 2: Using Graphs and Tables to Describe Data .......................................................9 Chapter 3: Using Numbers to Describe Data.....................................................................51

Part III: Building Blocks for Inferential Statistics Chapter 4: Calculating Probabilities ..................................................................................65 Chapter 5: Probability Distributions ..................................................................................88 Chapter 6: Using Sampling Distributions to Make Decisions .........................................108

Part IV: Making Decisions Chapter 7: Making Decisions with a Single Sample .......................................................129 Chapter 8: Estimating Population Values ........................................................................161 Chapter 9: Making Decisions with Matched Pairs Samples, Quantitative or Ranked Data..........................................................................194 Chapter 10: Making Decisions with Two Independent Samples, Quantitative or Ranked Data..........................................................................234 Chapter 11: Making Decisions with Three or More Samples, Quanitative Data—Analysis of Variance (ANOVA) ....................................275 Chapter 12: Making Decisions with Two or More Samples, Qualitative Data .............................................................................................321

Part V: Analyzing Relationships Chapter 13: Analyzing Linear Relationships, Two Quantitative Variables ....................351 Chapter 14: Analyzing Linear Relationships, Two or More Variables ...........................389

Instructor’s Solutions Manual - Chapter 1

Chapter 1 Solutions Develop Your Skills 1.1 1. You would have to collect these data directly from the students, by asking them.

This would be difficult and time-consuming, unless you are attending a very small school. You might be able to get a list of all the students attending the school, but privacy protection laws would make this difficult. No matter how much you tried, you would probably find it impossible to locate and interview every single student (some would be absent because of illness or work commitments or because they do not attend class regularly). Some people may refuse to answer your questions. Some people may lie about their music preferences. It would be difficult to solve some of these problems. You might ask for the school's cooperation in contacting students, but it is unlikely they would comply. You could offer some kind of reward for students who participate, but this could be expensive. You could enter participants' names in a contest, with a music-related reward available. None of these approaches could guarantee that you could collect all the data, or that students would accurately report their preferences. One partial solution would be to collect data from a random sample of students, as you will see in the discussion in Section 1.2 of the text.

Without a list of all students, it would be difficult to ensure that you had a truly random sample, but this approach is probably more workable than a census (that is, interviewing every student).

2.

Because you need specific data on quality of bicycle components, you would need to collect primary data. Customer complaints about quality are probably the only source of secondary data that you would have.

3.

Statistics Canada has a CANSIM Table 203-0010, Survey of household spending (SHS), household spending on recreation, by province and territory, annual, which contains information on purchases of bicycles, parts and accessories.

There is a U.S. trade publication called "Bicycle Retailer & Industry News", which provides information about the industry. See http://www.bicycleretailer.com/. Access is provided through the Business Source Complete database.

Industry Canada provides a STAT-USA report on the bicycle industry in Canada, at http://strategis.ic.gc.ca/epic/internet/inimr-ri.nsf/en/gr105431e.html. Somewhat outdated information is also available at http://www.ic.gc.ca/eic/site/sgas.nsf/eng/sg03430.html.

Canadian Business magazine has a number of articles on the bicycle industry. One of the most recent describes the purchase of the Iron Horse Co. of New York by Dorel Industries (a Montreal firm). http://www.canadianbusiness.com/markets/headline_news/article.jsp?content=b1560 9913

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Instructor’s Solutions Manual - Chapter 1

4.

Although Statistics Canada takes great care in its data collection, errors do still occur, and data revisions are required. An interesting overview of GDP data quality for seven OECD countries is available at http://www.oecd.org/dataoecd/20/26/34350524.pdf You should be able to locate other information about data revisions. See also http://www.statcan.ca/english/about/policy/infousers.htm which describes Statistics Canada’s policy on informing users about data quality.

5.

At least some of the secondary data sources listed in Section 1.1 should help you. If you cannot locate any secondary data, get help from a librarian.

Develop Your Skills 1.2 6. The goal for companies is to create population data, but it is unlikely that every customer is captured in any CRM database. There are many examples of companies using CRM data. A search of the CBCA database on August 7, 2009 produced a list of 102 articles (for 2009) that contained “customer relationship management” as part of their citation and indexing. For example, the publication called "Direct Marketing" regularly writes about database marketing, data mining, and web analytics. See http://www.dmn.ca/index.html.

7.

This is a nonstatistical sample, and could be described as a convenience sample. The restaurant presumably has diners on nights other than Friday, and none of these could be selected for the sample. The owner should not rely on the sample data to describe all of the restaurant's diners, although the sample might be useful to test reaction to a new menu item, for example.

8.

These are sample statistics, as they are based on sample data. It would be impossible to collect data from all postsecondary students.

9.

Follow the instructions for Example 1.2c. The random sample you get will be different, but here is one example of the 10 names selected randomly.

AVERY MOORE EMILY MCCONNELL HARRIET COOGAN DYLAN MILES TERRY DUNCAN GEORGE BARTON JAMES BARCLAY AVA WORTH PAIGE EATON JORDAN BOCK

10. First, Calgary Transit will probably find it impossible to establish a frame for its target population, which is people with disabilities who use Calgary Transit. It will also have to carefully define what it means by “people with disabilities”. If this

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Instructor’s Solutions Manual - Chapter 1

means “people in wheelchairs”, then it will at least be possible to identify such riders when an interviewer visits a bus or a bus stop. However, it will be quite difficult for Calgary Transit to obtain a truly random sample of the opinions of people in wheelchairs who use Calgary Transit’s services. Coverage errors will be practically unavoidable. As well, if interviewers are approaching only those riders in wheelchairs, the survey respondents may be unhappy about being singled out because of their wheelchairs. They may refuse to answer the interviewer’s questions, leading to nonresponse errors. Interviewers will have to be trained carefully to overcome any resulting resistance of survey subjects. Because data will probably be collected on buses or at bus stops, with interviewers recording information while a bus is in motion, or possibly during bad weather at a bus stop, processing errors may occur. Finally, Calgary Transit will have to be sure that suitably qualified people are doing the analysis, to avoid estimation errors.

Develop Your Skills 1.3 11. This is impossible. A price cannot decrease by more than 100% (and a 100% decrease would mean the price was 0). It is likely that the company means that the old price is 125% of the current price. So, for example, if the old price was $250, then the new price would be $200. You can see that 250/200 = 1.25 or 125%.

12. The graph with the y-axis that begins at 7,000 is misleading, because it makes the index decline at the end of 2008 look more dramatic than it actually was. While the fall in the stock market index was significant, using a y-axis that begins with zero puts it in better perspective.

13. “Jane Woodsman’s average grade has increased from 13.8% last semester to 16.6% this semester.” The provocative language of the initial statement (“astonishing progress”, “substantial 20%”) is inappropriate. As well, the 20% figure, used as it is here, suggests something different from the facts. Jane’s grades did increase by 20%, but this is only 20% of the original grade of 13.8%, so it is not much of an improvement.

14. Aside from the fact that you should be suspicious of anyone who will not share the actual data with you, the local manager’s assurance that all is well may not be borne out by fact. Notice that the decrease claimed is in maximum wait times, not average wait times. It is entirely possible that average waiting times have increased. You need to see the data!

15. Yes. There is no distortion in how the data are represented, and the graph is clearly labelled and easy to understand.

Develop Your Skills 1.4 16. No! With such an observational study, this kind of conclusion about cause and effect is not justified. There could be many factors (other than income) that explain why children in wealthier families are better off. For example, parents in wealthier

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Instructor’s Solutions Manual - Chapter 1

families may have more confidence, and this may provide a very positive environment in which children flourish.

17. No. Even if the study was randomized (no information is provided), it would not be legitimate to make this conclusion. While taller men are more likely to be married, we cannot conclude that they are more likely to be married because they are taller.

There could be many other factors at work.

18. It may be that the diary system contributed to increased sales. Because the data compares the same people before and after use of the diary system, there is some support for this conclusion. However, notice that only poor performers were selected for the trial. These people may have worked harder simply because it was clear their poor performance had been noticed.

19. If you had compared the sales performance of a randomly-selected group of salespeople (not only poor performers), you would be able to come to a stronger conclusion about the diary system’s impact on increased sales.

20. There may be a cause-and-effect relationship here, but any conclusions should be made cautiously. For example, hotter weather or a nearby fair for children could have increased foot traffic (and sales) during the period.

Develop Your Skills 1.5 21.a.

In this case, the national manager of quality control probably has a good grasp of statistical approaches. While you should still strive for clarity and simplicity, you can include more of the technical work in the body of the report. Printouts of computer-based analysis would be included in the appendix. b. While human resources professionals probably have some understanding of statistical analysis, they are less likely to understand the details. In this case, you should write your report with a minimum of statistical jargon. The body of your report should contain key results, but the details of your analysis should be saved for the appendix. c. In this case, you can assume no statistical expertise in your readers. While you should still report on how your analysis was conducted, and how you arrived at your conclusions, you probably would not send this part of the report to your customers.

The report you send your boss should be easily understandable to everyone. The challenge here will be to make your conclusions easy to understand, while not oversimplifying, or suggesting that your results are stronger than they actually are.

22. It is incorrect to suggest that study of a random sample “proves” anything. This statement is much more definitive than can be justified. As well, the study was done on past customers, and may not apply to future customers. Nevertheless, such a study could be persuasive about what segment of the market the company should focus on.

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Instructor’s Solutions Manual - Chapter 2

Develop Your Skills 2.2 6. The three different histograms are shown below.

Survey of Drugstore Customers: Customer Ages 18 16

Number of Customers

14 12 10 8 6 4 2 0

Age of Customers

Survey of Drugstore Customers: Customer Ages Number of Customers

30 25 20 15 10 5 0

Age of Customers

Survey of Drugstore Customers: Customer Ages 35

Number of Customers

30 25 20 15 10 5 0

Age of Customers

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Instructor’s Solutions Manual - Chapter 2

All three histograms clearly show that the distribution of customer ages is skewed to the right, that is, while most customers are under 40 years old, there are some customers who are much older, in fact as old as 85. [Note that when you are describing the distribution, it is not sufficient to stop at “skewed to the right”—you should explain what this means, in the context of this particular data set.]

A class width of 5 is not a good choice for this data set. There are too many classes, many of which have only a very few data points. A class width of 10 or 15 would be a better choice.

7.

A frequency distribution and histogram are shown below.

Survey of Drugstore Customers Customer Income Number of Customers $30,000 to <$35,000 2 $35,000 to <$40,000 9 $40,000 to <$45,000 14 $45,000 to <$50,000 7 $50,000 to <$55,000 5 $55,000 to <$60,000 6 $60,000 to <$65,000 3 $65,000 to <$70,000 4

Survey of Drugstore Customers: Customer Incomes 16 14

Number of Customers

12 10 8 6 4 2 0

Customer Income

Choosing class widths is a bit tricky in this instance. The class width template suggests class widths of 5155, 8905, or 8170. None of these numbers is that comfortable for incomes. Class widths of $5,000 and $10,000 were considered. A class width of $10,000 was discarded because it would have resulted in only four classes (five is a good minimum number of classes).

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SOLUTIONS MANUAL FOR Analyzing Data and Making Decisions Statistics for Business Microsoft Excel 2010 Updated 2nd Edition. Judith Skuce https://scholarfriends.com/singlePaper/427503/sol utions-manual-for-analyzing-data-and-makingdecisions-statistics-for-business-microsoft-excel201