#1

   

Case Study: MBA Schools in Asia-Pacific

The pursuit of a higher education degree in business is now international. A survey shows more and more Asians choose the master of business administration (MBA) degree route to corporate success. As a result, the number of applicants for MBA courses at Asia-Pacific schools continues to increase.

Across the region, thousands of Asians show an increasing willingness to temporarily shelve their careers and spend two years in pursuit of a theoretical business qualification. Courses in these schools are notoriously tough and include statistics, economics, banking, marketing, behavioral sciences, labor relations, decision making, strategic thinking, business law, and more. 

After your MBA, you get a job at Bloomberg in its media division, Bloomberg Business. Your division publishes reviews and rankings for business schools in the US and internationally. Because of your strong analytical education from University of Phoenix, your boss assigns you to work on preparing an analysis for data gathered for leading business schools in the Asia-Pacific. The data set in the Excel® file shows some of the characteristics of the leading Asia-Pacific business schools. 

Resources: Microsoft Excel®, Case Study: MBA Schools in Asia-Pacific

Review the Case Study: MBA Schools in Asia-Pacific and the

Case Study: MBA Schools in Asia-Pacific Data set

. 

Prepare a 1,050-word managerial report for your boss.

Use the following questions for guidelines and directions on what to include in the report:

1. What is the type of data (Quantitative or Qualitative) for each of the columns (variables) in the dataset? If quantitative, is the data discrete or continuous? Neatly summarize your response in a table for all the columns (variables).
2. Using Excel®, find the mean, median, standard deviation, minimum, maximum, and the three quartiles for each of the quantitative variables identified in part 1 above. Neatly summarize in a table on this document. Comment on what you observe.
3. What are the minimum and maximum full-time enrollments? Which schools have the minimum and maximum full-time enrollments?
4. What is the average number of students per faculty member? Is this low or high? What does this mean to prospective applicants who are interested in pursuing an MBA in one of the leading international business schools?
5. What are the mean, median, and modal ages? What does this mean to prospective applicants?
6. What is the mean percentage of foreign students? How many and which schools have 1% and 0% foreign students? Which schools have highest percentage of foreign students? Please state these percentages.
7. What percentage of schools require the GMAT test?
8. What percentage of schools require English tests such as Test of English as a Foreign Language (TOEFL)?
9. What percentage of schools require work experience? From this percentage, does this appear to be a significant factor in gaining admissions?
10. What are the mean and median starting salaries? Which schools have the minimum and maximum starting salaries? How much are these minimum and maximum salaries?
11. What are the mean tuition for foreign students and for local students? Does there appear to be a significant difference? What is the difference between the two means?
12. How many schools require work experience and how many of them don’t? What is the mean starting salary for schools requiring work experience? What is the mean starting salary for schools requiring no work experience?
13. How many schools require English tests and how many don’t? What is the mean starting salary for schools requiring English tests? What is the mean starting salary for schools requiring no English tests?
14. Comment on the skewness for the data on starting salaries:
15. Plot a histogram and determine the skewness.
16. Find the skewness coefficient.
17. Find the mean, median, and mode for starting salaries and compare the three measures to determine skewness.
18. Finally, use Empirical Rule on the starting salaries and determine whether the salaries follow the Empirical Rule.

Format your assignment consistent with APA format.

 

#2
   

Case Study – Bell Computer Company

The Bell Computer Company is considering a plant expansion enabling the company to begin production of a new computer product. You have obtained your MBA from the University of Phoenix and, as a vice-president, you must determine whether to make the expansion a medium- or large- scale project. The demand for the new product involves an uncertainty, which for planning purposes may be low demand, medium demand, or high demand. The probability estimates for the demands are 0.20, 0.50, and 0.30, respectively.

Case Study – Kyle Bits and Bytes

Kyle Bits and Bytes, a retailer of computing products sells a variety of computer-related products. One of Kyle’s most popular products is an HP laser printer. The average weekly demand is 200 units. Lead time (lead time is defined as the amount of time between when the order is placed and when it is delivered) for a new order from the manufacturer to arrive is one week. 

If the demand for printers were constant, the retailer would re-order when there were exactly 200 printers in inventory. However, Kyle learned demand is a random variable in his Operations Management class. An analysis of previous weeks reveals the weekly demand standard deviation is 30. Kyle knows if a customer wants to buy an HP laser printer but he has none available, he will lose that sale, plus possibly additional sales. He wants the probability of running short (stock-out) in any week to be no more than 6%.

Resources: Microsoft Excel®,

Bell Computer Company Forecasts

data set,

Case Study Scenarios

Write a 1,050-word report based on the Bell Computer Company Forecasts data set and Case Study Scenarios. 

Include answers to the following: 

Case 1: Bell Computer Company

• Compute the expected value for the profit associated with the two expansion alternatives. Which decision is preferred for the objective of maximizing the expected profit?
• Compute the variation for the profit associated with the two expansion alternatives. Which decision is preferred for the objective of minimizing the risk or uncertainty? 

Case 2: Kyle Bits and Bytes

• What should be the re-order point? How many HP laser printers should he have in stock when he re-orders from the manufacturer?

Format your assignment consistent with APA format.
#3
   

Major consulting firms such as Accenture, Ernst & Young Consulting, and Deloitte & Touche Consulting employ statistical analysis to assess the effectiveness of the systems they design for their customers. In this case, a consulting firm has developed an electronic billing system for a Stockton, CA, trucking company. The system sends invoices electronically to each customer’s computer and allows customers to easily check and correct errors. It is hoped the new billing system will substantially reduce the amount of time it takes customers to make payments. Typical payment times—measured from the date on an invoice to the date payment is received—using the trucking company’s old billing system had been 39 days or more. This exceeded the industry standard payment time of 30 days.

The new billing system does not automatically compute the payment time for each invoice because there is no continuing need for this information. The management consulting firm believes the new system will reduce the mean bill payment time by more than 50 percent. The mean payment time using the old billing system was approximately equal to, but no less than, 39 days. Therefore, if µ denotes the new mean payment time, the consulting firm believes that µ will be less than 19.5 days. Therefore, to assess the system’s effectiveness (whether µ < 19.5 days), the consulting firm selects a random sample of 65 invoices from the 7,823 invoices processed during the first three months of the new system’s operation. Whereas this is the first time the consulting company has installed an electronic billing system in a trucking company, the firm has installed electronic billing systems in other types of companies.

Analysis of results from these other companies show, although the population mean payment time varies from company to company, the population standard deviation of payment times is the same for different companies and equals 4.2 days. The payment times for the 65 sample invoices are manually determined and are given in the Excel® spreadsheet named “The Payment Time Case”. If this sample can be used to establish that new billing system substantially reduces payment times, the consulting firm plans to market the system to other trucking firms.

Resources: Microsoft Excel®,

The Payment Time Case Study

,

The Payment Time Case Data Set

Review the Payment Time Case Study and Data Set. 

Develop a 700-word report including the following calculations and using the information to determine whether the new billing system has reduced the mean bill payment time:

Assuming the standard deviation of the payment times for all payments is 4.2 days, construct a 95% confidence interval estimate to determine whether the new billing system was effective. State the interpretation of 95% confidence interval and state whether or not the billing system was effective.

Using the 95% confidence interval, can we be 95% confident that µ ≤ 19.5 days?

Using the 99% confidence interval, can we be 99% confident that µ ≤ 19.5 days?

If the population mean payment time is 19.5 days, what is the probability of observing a sample mean payment time of 65 invoices less than or equal to 18.1077 days? 

#4
   

Case Study – Election Results

When an election for political office takes place, the television networks cancel regular programming and instead, provide election coverage. When the ballots are counted, the results are reported. However, for important offices such as president or senator in large states, the networks actively compete to see which will be the first to predict a winner. This is done through exit polls, wherein a random sample of voters who exit the polling booth is asked for whom they voted. From the data, the sample proportion of voters supporting the candidates is computed. Hypothesis testing is applied to determine whether there is enough evidence to infer the leading candidate will garner enough votes to win. 

Suppose in the exit poll from the state of Florida during the 2000 year elections, the pollsters recorded only the votes of the two candidates who had any chance of winning: Democrat Al Gore and Republican George W. Bush. In a sample of 765 voters, the number of votes cast for Al Gore was 358 and the number of votes cast for George W. Bush was 407. The network predicts the candidate as a winner if he wins more than 50% of the votes. The polls close at 8:00 P.M. Based on the sample results, conduct a one-sample hypothesis test to determine if the networks should announce at 8:01 P.M. the Republican candidate George W. Bush will win the state.

Use 0.10 as the significance level (α).

Case Study – SpeedX

SpeedX, a large courier company, sends invoices to customers requesting payment within 30 days. The bill lists an address, and customers are expected to use their own envelopes to return their payments. Currently, the mean and standard deviation of the amount of time taken to pay bills are 24 days and 6 days, respectively. The chief financial officer (CFO) believes including a stamped self-addressed envelope would decrease the amount of time. She calculates the improved cash flow from a 2-day decrease in the payment period would pay for the costs of the envelopes and stamps. You have an MBA from the University of Phoenix, and work for SpeedX as a business analyst. One of your job duties is to run analytics and present the results to the senior management for critical decision-making. You see this as an opportunity to utilize some of the skills you gained in the Statistics course. Because of your strong understanding and background in inferential statistics, you decide to take up this important assignment. You have learned any analysis in inferential statistics starts with sampling. To test the CFO’s belief, you decide to randomly select 220 customers and propose to include a stamped self-addressed envelope with their invoices. The CFO accepts your proposal and allows you to run a pilot study. You then record the numbers of days until payment is received. Using your statistical expertise and skills you gained in the class, conduct a one-sample hypothesis test and determine if you can convince the CFO to conclude that the plan will be profitable.

Use 0.10 and the significance level (α).

Resources: Microsoft Excel®, Case Study Scenarios,

SpeedX Payment Times

Develop a 700- to 1,050-word statistical analysis based on the Case Study Scenarios and SpeedX Payment Times. 

Include answers to the following: 

Case 1: Election Results 

Use 0.10 as the significance level (α).
• Conduct a one-sample hypothesis test to determine if the networks should announce at 8:01 P.M. the Republican candidate George W. Bush will win the state. 

Case 2: SpeedX

Use 0.10 and the significance level (α).
• Conduct a one-sample hypothesis test and determine if you can convince the CFO to conclude the plan will be profitable. 

Format your assignment consistent with APA format. 

#5  

When an election for political office takes place, the television networks cancel regular programming and instead, provide election coverage. When the ballots are counted, the results are reported. However, for important offices such as president or senator in large states, the networks actively compete to see which will be the first to predict a winner. This is done through exit polls, wherein a random sample of voters who exit the polling booth is asked for whom they voted. From the data, the sample proportion of voters supporting the candidates is computed. Hypothesis testing is applied to determine whether there is enough evidence to infer the leading candidate will garner enough votes to win. 
Suppose in the exit poll from the state of Florida during the 2000 year elections, the pollsters recorded only the votes of the two candidates who had any chance of winning: Democrat Al Gore and Republican George W. Bush. In a sample of 765 voters, the number of votes cast for Al Gore was 358 and the number of votes cast for George W. Bush was 407. The network predicts the candidate as a winner if he wins more than 50% of the votes. The polls close at 8:00 P.M. Based on the sample results, conduct a one-sample hypothesis test to determine if the networks should announce at 8:01 P.M. the Republican candidate George W. Bush will win the state. Use 0.10 as the significance level (α).
Case Study – SpeedX
SpeedX, a large courier company, sends invoices to customers requesting payment within 30 days. The bill lists an address, and customers are expected to use their own envelopes to return their payments. Currently, the mean and standard deviation of the amount of time taken to pay bills are 24 days and 6 days, respectively. The chief financial officer (CFO) believes including a stamped self-addressed envelope would decrease the amount of time. She calculates the improved cash flow from a 2-day decrease in the payment period would pay for the costs of the envelopes and stamps. You have an MBA from the University of Phoenix, and work for SpeedX as a business analyst. One of your job duties is to run analytics and present the results to the senior management for critical decision-making. You see this as an opportunity to utilize some of the skills you gained in the Statistics course. Because of your strong understanding and background in inferential statistics, you decide to take up this important assignment. You have learned any analysis in inferential statistics starts with sampling. To test the CFO’s belief, you decide to randomly select 220 customers and propose to include a stamped self-addressed envelope with their invoices. The CFO accepts your proposal and allows you to run a pilot study. You then record the numbers of days until payment is received. Using your statistical expertise and skills you gained in the class, conduct a one-sample hypothesis test and determine if you can convince the CFO to conclude that the plan will be profitable. Use 0.10 and the significance level (α).
 

Resources: Microsoft Excel®,

Signature Assignment Databases

,

Signature Assignment Options

,

Part 3: Inferential Statistics

Scenario: Upon successful completion of the MBA program, imagine you work in the analytics department for a consulting company. Your assignment is to analyze one of the following databases:

• Manufacturing
• Hospital
• Consumer Food
• Financial 

Select one of the databases based on the information in the Signature Assignment Options. 

Provide a 1,600-word detailed, four part, statistical report with the following sections:

Part 1 – Preliminary Analysis

Part 2 – Examination of Descriptive Statistics
Part 3 – Examination of Inferential Statistics
Part 4 – Conclusion/Recommendations 

Part 1 – Preliminary Analysis

Generally, as a statistics consultant, you will be given a problem and data. At times, you may have to gather additional data. For this assignment, assume all the data is already gathered for you.

State the objective:

• What are the questions you are trying to address?

Describe the population in the study clearly and in sufficient detail:

• What is the sample?

Discuss the types of data and variables:

• Are the data quantitative or qualitative?
• What are levels of measurement for the data? 

Part 2 – Descriptive Statistics 

Examine the given data.

Present the descriptive statistics (mean, median, mode, range, standard deviation, variance, CV, and five-number summary).

Identify any outliers in the data.

Present any graphs or charts you think are appropriate for the data.

Note: Ideally, we want to assess the conditions of normality too. However, for the purpose of this exercise, assume data is drawn from normal populations. 

Part 3 – Inferential Statistics

Use the Part 3: Inferential Statistics document.

• Create (formulate) hypotheses
• Run formal hypothesis tests
• Make decisions. Your decisions should be stated in non-technical terms.

Hint: A final conclusion saying “reject the null hypothesis” by itself without explanation is basically worthless to those who hired you. Similarly, stating the conclusion is false or rejected is not sufficient. 

Part 4 – Conclusion and Recommendations

Include the following:

• What are your conclusions?
• What do you infer from the statistical analysis?
• State the interpretations in non-technical terms. What information might lead to a different conclusion?
• Are there any variables missing?
• What additional information would be valuable to help draw a more certain conclusion?

Format your assignment consistent with APA format. 

2

>Case

0

,

0

,

0

0

Yes

,400

8

4

,993

,582

Yes No Yes

0

5

0

4,

22 0 No No No

0

5

0

11,140 29 10 Yes No No

4

33,060 28 60 Yes Yes No

5

Yes No Yes

0

5

1 Yes No No

6

0

29

Yes Yes Yes

,300

8

16,000 23 0 No No No

42 2

20,300 30 80 Yes Yes Yes

50 5

8,500 32 20 Yes No Yes

8

17 16,000

32 26 No No Yes

60 2

11,513 26

Yes No Yes

,000

8

34

No No Yes

7

17,355 25 6 Yes No Yes

13

30 30 Yes Yes Yes

300 10

18,200 29 90 No Yes Yes

20 19

30 10 No No Yes

30

37 35 No Yes Yes

30 7

32 30 No Yes Yes

0

9 1,000 1,000 24 0 No No Yes 7,000

15

29 43 Yes No Yes

14

23

No No No 7,500

30 5

2,260 32 15 No Yes Yes 16,000

17

28

Yes No Yes

Business School	Full-Time Enrollment	Students per Faculty	Local Tuition ($)	Foreign Tuition ($)	Age	%Foreign	GMAT	English Test	Work Experience	Starting Salary ($)
Melbourne Business School	2			0							5	2		4	42					2	9			6			2		8	4		7																																									Yes																																		No	7	1
University of New South Wales (Sydney)		22		19					32	29	28	65,		20
Indian Institute of Management (Ahmedabad)	392		4,								30	300	7,			10
Chinese University of Hong Kong		90		11,	14	3		1,000
International University of Japan (Niigata)	1		26		33,0	60	8	7,000
Asian Institute of Management (Manila)	389	7,562	9,000		25		50	22,	80
Indian Institute of Management (Bangalore)	380	3,9	35				16,000			23		7,500
National University of Singapore	147	6,146	7,		17	51		43
Indian Institute of Management (Calcutta)	463	2,880	7,400
Australian National University (Canberra)		20,300	46,600
Nanyang Technological University (Singapore)		8,500	49,300
University of Queensland (Brisbane)		13	22,800	49,600
Hong Kong University of Science and Technology		11,513		37		34
Macquarie Graduate School of Management (Sydney)	12	17,172	19,778	27	60,100
Chulalongkorn University (Bangkok)	200		17,355	17,600
Monash Mt. Eliza Business School (Melbourne)	350	16,200	22,500	52,500
Asian Institute of Management (Bangkok)		18,200	25,000
University of Adelaide	16,426	23,100	66,000
Massey University (Palmerston North, New Zealand)			15	13,106	21,625	41,400
Royal Melbourne Institute of Technology	13,880	17,765	48,900
Jamnalal Bajaj Institute of Management Studies (Bombay)		24
Curtin Institute of Technology (Perth)	98	9,475	19,097	55,000
Lahore University of Management Sciences	70	11,250	26,300	2.5
Universiti Sains Malaysia (Penang)		2,260
De La Salle University (Manila)	44	3,300	3,600	3.5	13,100

Case 1

-Scale

Expansion Profits

00s)

Annual Profit
($

0s) P(x)

0 20%

Medium

100 50%

30%

Demand

Low 50 20%
Medium 150 50%
High 200 30%

Demand Annual Profit (x)
$1000s Probability P(x) (x – µ) (x – µ)2 (x – µ)2 * P(x)

Low 0 20%
Medium 100 50%
High 300 30%

σ2 =
σ =

Medium

Large-Scale

Expansion Profits

Annual Profit ($1

0

P(x)

100

Demand

Low

50

20%

150

50%

High

200

30%

300

Expected Profit ($1000s)

Risk Analysis for Medium-Scale Expansion

Annual Profit (x) $1000s

Probability P(x)

(x – µ)

(x – µ)2

(x – µ)2 * P(x)

σ2 =

σ =

Risk Analysis for Large-Scale Expansion

The Payment Time Case

16

18

16

16
22
17

15
21

16
15
19
18
15
22
16

20
17

14
19
15

17
25
13
17
16
13
18
19
18
14
17
17
12
23
24
18
16
16
20
15
24
17
21
15
14
19

21

PayTime
		22
				19
								16
					18
		13
29
							17
						15
	23
			21
10
	25
		20
		24
			14
27
	12
26

Sheet1

13

24

13
23

14
27

20
30
24
18
21
24
18
27
27
27
21
22
23
18
17
23
26
20
20
22
21
13

18

26
19
16

16
20
16
14
25
14

17
16
19
19
17
18
22
23
22
27
23
23
21
20
18
29
32
27

21
26
32
20
29
25
15
21
30
24
23
14
18
22

24
35
29
24
17
27
15
19

19
21
19
21
15
17
20
21
31
19
27
19
26
26
26
23
12
20
34
21
24
20
21
16
23
13
19
18
31
29
23
28
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19
22
24
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25
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22
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25
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16
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25
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29
21
22
11
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19
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22
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31
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27
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25
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26
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21
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16
25
13
11
13
22
28
14
21
30
19
14
31

9

14
21
28

Payment
											27
												24
										14
3	9
							13
				31
								26
33
														23
										17
											18
	34
									16
		32
						30
					29
																			21
															19
												22
									20
		11
36
									25
				28
	35
						15
37
			12
10

2

>Option

1

– Manufacturer

1

3

0

7

1

2

4

7

1

6

2

1

204

0

6

0

07

1

30

1

40

74

3

1

207 26

8

6

1

3

72

10

21

1

1

8

12

5

1

21 15

2

7

2

3 2

7

42 2

2 2

8

2

6 4

2

2

52

3

222 74 63

07

57

7

3

13 12

1

3

17 13

7

3

9

21

3

3

55 44

3

76

06

4

3

61 47

3

27 22

4

6

4

45

7

4

38 32

6

2

4

17 14

9

4

34 28

3

450 4

1 1 34 71 17 4

31 25

5

4

224

3

76

7

4

83 68

5

1

5

147

5

209

4

32

5

51 43

9

5

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6

5

94 78

5

64

80

5

6

70 53

1

6

37 29

447 6

81 61

5

6

6

54 39

8

6

15 11

7

90

7

55 42

5

7

212

68

7

267 232 182

63

7

92

3

8

272 121 16

82

8

273 136 57

7

6

8

274 69 25

3

8

604

07

72

8

276 41 28

8

1

8

21 12

9

577 8

65 50

504 8

279 55 39

8

236 8

281 80 45

67

98

9

282

79

96

9

283 213 106

13

3

9

126 75

1

32

9

285 51 28

9

126 75

86

5

9

37 24

77

30

9

289 76 45

7

5

9

291 67 43

6

80

18

10

295 25 18

4

10

14 8

10

65 54

11

8 7

11

61 46

7

11

306 122 95

1

11

308

598

21

74

11

15 12

3

404 12

313 3 2

163 35 12

314 37 31

7

2

716 12

2 2 53 85 62 12

6 4

199 12

8 7 328

75 12

7 6 233

40 12

321 12 9

7

282 13

60 51

7

13

64 50

13

17 13

13

325 31 25

7

13

45 36

600 13

327 205

39

0

6

13

328 17 13

263 13

329 72 53

13

221

96

14

106

14

35 26

14

15 11

694 14

162 123

70

2

14

94 79

14

32 23

14

33 27

9

3

15

140 107

50

4

15

45 32

3527

15

432 315

4

15

345 104 81

15

346 259

31

15

129 99

15

40 24

15

300 219

8

15

79 55

9

16

352 94 70

8

16

353 205

8

16

354 295 211

43

16

355 192 110

1

16

356 265 172

8

16

259 96

5

7

7

16

201 147

16

16

74 51

5

17

171 120

17

87

17

364 157 117

28

5

17

49 37

1

17

120

1

17

367

17

106

20

17

634

9

52

18

377 190

4

18

373 141 108

0

18

31 23

18

18 14

4

412 18

81 29

18

47 35

18

381 186 68

19

272 141

19

384

157

51

19

27 17

19

61 36

2290 19

387 6 4

382 177 19

43 30

20

13 10

506 328 20

76

4

20

395 35 26

20

24 19

997 415 20

399 179 123

9

20

SIC Code	No. Emp.	No. Prod. Wkrs.	Value Added by Mfg.	Cost of Materials	End Yr. Inven.	Indus. Grp.
							2																																																												0																																																																																																		4																																																																																																																																																																											3	3																																										7						23																																										5	1														8						78																	13	3																																														6				30
	20	1							31				83																	15				72					42					74	31		57
		203		204	1		6												9	2	4						50							27				22			87					32
						10			70						21		67							37					40					34
			205	220		137			207														12					120	1	1					55
206		89			69	1					26			1				36	3							61
												18	4							25												19		130	1					94
208																14	3			52	3						35			71			99
	209																							17							126	20				54	1				96		313
2											11	23		44		555	5	506
	212										28	1				63
	213			150		314	155
214	6					24		2			62	554
	221								47	2471	4		219	9						29
								43					53			142
	223	6	73							106				325
	224					81	707		267
225																16					147	89			86					104	2083
226								51			41	31						45	4				140	6	97
227	40						76	7		125	14			46
	228				84					38	8994			101
	229	4	276	5	504	1	2			91
		231	1	2				39		716			3			56
		232		200		178	9423	8	92	2314
		2		33	294	250			110	11		121			272
	234			191	2	283							68
235			59	3				64		197
	236	2063					181
237
238						144	1	321	526
239	1					79	10			60					123			274
	241		5			77	9				66	578
242				172	10	404	19	2				85	3979
		243		257	1	327				186	3	329
	244	1		90	2		170			355
	245					82		460	7290	5				80
2				49	5518	8	135	1	604
	251			273		233				124					129		353
252	5			447		401	829
		253		2290	5101
		254	4		182	3					75			95
		259		281	2	694	718
	261	2201	3	279	725
	262		116	1	88			48	20	596	4257
		263	9				65	10604	1	502
		265				163			156	24	634	3976
25918		289	5427
271	403		136			306		848	894
					179	6940	1		216
1	785	8863			373
9	699			282	874
275		437		384					295	4		300
		387		381	688
277	3	98	1047
278	4388	2055
4055			109
		165				112	2644
		115	25025		345	6		192
	598	27		187	1		153
	284	3			180			199	4	535
8497	9849		2178
	286		288	46			93	8577
287			122				111	2		354
1			154	1		308	2749
2	600	1					328				107
	346	6182	6	58
299		2187	4446		670
301	7079	7091		1067
	302	442			496			175
305	4528		3805		105
7275	7	195				141
763			556	57264		118
311				131	1865
		162
		190				168
	315
		316	747				395
317	255
	319			177
			171		943
	322	6532					352	1	505
323	4850	4254	883
324	3	509	2282	828
2	176				138	700
	326	2696	1	183
	152				157	1	701		196
999	565
7	838	5		432	1652
331			174	29180		456	12			198
332		128	9061	6913		1543
333	4200	11		184	1	834
334	1410	5	735
	335		166	3		189	6		377
	336	5856	4696	938
			339	3	164	2		790	800
341			399	9		364				145
	342			117	8720		312
343	4	412	1121
344	2	797	31527		7204
6	936	4909	1768
		211	19880		215	3	997
		347	7		793	6232	1181
348	3528		1689			1077
349		2171	19273	6460
351	10513	12		954		367
9		545	1				185	3339
	133	1	817	23474	7344
22673		143	6730
1	922	16515	6823
23110	18543		789
357	4			113	60857					102
358	17521	2	1819	4857
359		392	293		25322	13897	4964
361	6700	5	523		149
	362	14278	12657	3887
363					108	9	466	1	2578		2299
	134			1106	3076
365	3459		762	1070
366	258	38705	2	959	9467
588	368	84059	44486	13145
369		151			139	13	398	3	514
371	772	10	589	223639		158
372	45220	42367		3681
7903		776	2165
374	2590	4363	1233
	375	1435			167
	376	9986	8120	4770
379	3564	5476	1102
2		1071	8760	6183
	382	29028	18028	7681
268		310	16787		7761
385	2	390	1020	426
	386	14032	8		114
			415
391	2761	3646	1		451
393	685
394		103	8327		660	2608
2643	1789		799
	396	1406
1		119	8530	2861

Option 2 –

Hospital

Hospital

1 1 2 1 107 312

2 1 1 1 198 1077

3 1 2 1 356 1027

4 1 1 1

355 328

5 7 1 1 9 168 181
6 4 2 1

1077

7 4 4 1 65 735

8 4 2 1 48 1 131
9 1 2 1 253

3

10 1 1 1 21 257 233
11 1 1 1 27

241

12 6 3 1 30

203

13 6 3 1 43 0 325
14 6 2 1 233

15 6 4 1 2 211 347
16 6 1 1 11 16 79
17 6 3 1 84

505

18 6 2 1 219

1543

19 6 3 1 112

5

20 6 3 1 124 0 959
21 6 3 1 50

325

22 6 2 1 142

954

23 6 2 1 111

24 6 1 1 140

25 6 3 1 28 451 300
26 6 2 1 154 1689

27 6 2 1 150

28 6 3 1 144

29 6 3 1 42

354

30 6 2 1 77

31 5 2 1 119

32 5 2 1 27

386

33 5 2 1 15 126 144
34 2 2 1 179

6

35 2 2 1 175

36 2 2 1

37 1 3 2 32 0 96
38 1 2 1 74 0

39 1 1 1

40 1 2 1 253

41 1 3 1 180

1071

42 1 1 1 184 762

43 1 2 1 243

44 1 2 1 115 496 670
45 1 1 1 215

46 1 3 2 48 0 167
47 1 3 1 124

793

48 1 2 1 189

9

49 1 1 1 181 113 316
50 1 1 1 9 0 93
51 1 3 1 28 0 373
52 1 2 1 288 173 263
53 1 1 1 108

943

54 1 2 1 154

55 7 2 1 76

596

56 7 3 1 165

57 3 1 2 295 0

58 3 3 1 101 0

59 3 2 1 69

60 3 2 1 12 99 136
61 3 2 1 185

62 3 2 1

63 3 2 1 114

64 3 3 2 49 0 243
65 3 2 1 106

66 3 2 1 460

1

67 3 2 1 43 126

68 3 4 1 29 556

69 3 2 1 125

7

70 3 2 1 17 415 322
71 3 1 1 10 216 185
72 3 3 1 14 339 205
73 3 1 1 173

74 3 2 1 207

75 3 2 1 223 790

76 3 2 1 82

77 3 1 1 64 35 156
78 3 2 1 139

79 3 2 1 109 793

80 3 1 2 298 0 790
81 3 3 1 52 0 308
82 3 1 1 34 14 70
83 3 1 2 168 0 494
84 3 3 2 21 0 111
85 1 4 1 390 0

86 1 1 1 47 0 244
87 1 2 1 80 776 525
88 1 3 1 50 451

89 1 3 2 113 0 94
90 1 2 1 45 145

91 1 1 1 76

92 1 3 1 129 1 234
93 2 2 1 60 319 401
94 2 2 1

95 2 2 1 17 295 198
96 2 2 1 138 496

97 2 2 1 64 589 545
98 2 2 1 62

99 2 1 1 131 701

100 2 2 1 265

101 3 4 2 456 0

102 3 2 2 40 0 126
103 3 1 1 310

104 3 3 1 72 0 251
105 3 3 2 19 0 85
106 3 4 1 112 0 432
107 3 1 2 375 0

108 3 3 2 15 0 66
109 3 2 1 78

556

110 3 1 1 123 169 347
111 3 2 1 54 66 239
112 3 2 1 96

113 1 3 1 82

114 1 1 2 1106 0

115 1 1 1 30 0 102
116 3 1 2 56 0 262
117 3 4 1 36 342 885
118 3 3 1

494

119 3 1 2 180 0

120 3 4 1 59 0 330
121 5 2 1 127 0

122 5 1 1 37 0 75
123 5 4 1 13 286 262
124 5 2 1 100 235 328
125 3 3 1 47 339 377
126 3 1 1 194 398

127 3 2 1 172

128 5 3 1

129 5 3 1 120

130 5 1 1 179 714

131 2 4 1 140 0 535
132 2 3 2 78 0

133 2 3 2 68 0 202
134 2 2 1 186

135 2 2 1 91 0

136 2 1 1

137 5 1 1 254

138 5 2 1 108 1071 815
139 5 2 1 61 352

140 2 2 1 174 254 502
141 2 1 2 306 0

142 2 3 2 28 0 50
143 2 2 1 395 699

144 2 2 1

145 2 1 1 335

146 1 2 1 46 0 68
147 1 1 1 316

1 4 2

0

149 1 1 1 74 339 576
150 1 2 1 86 130 284
151 3 2 1 38 91 145
152 3 2 1 147

153 3 4 2 232 0

154 3 1 2 138 0 336
155 3 2 1 38 509 415
156 3 2 1 245

157 3 1 2 171 0

158 3 3 1 51 447

159 3 4 1 28 1161 437
160 3 1 2 797 0 261
161 7 2 1 56 922

162 7 1 1 69

163 7 1 1 40 78 61
164 7 4 1 163 0

165 7 2 1 231

166 2 1 2 523 0

167 2 4 1 31 0

168 2 2 2 43 0 153
169 2 2 1 66

170 2 2 1 231 1165

171 1 4 1 11 466

172 1 3 1 144 1106 789
173 1 3 1 43 376 395
174 3 4 1 185 0

175 3 2 1 82

362

176 1 2 2 49 0 144
177 1 3 1 24 352 229
178 1 3 1 63 447 396
179 1 2 1 274

180 1 3 1 93

181 1 4 1 86

182 1 3 2 28 0 102
183 1 3 2 25 0 106
184 1 3 1 181

185 5 3 1 39

392

186 5 1 1 302

187 5 2 1 80

785

5 2 1 63 2171 607

189 2 2 1 31 364 273
190 2 2 2 170 0

191 1 1 1 203

192 1 2 1

0

193 1 3 1 83

583

194 7 2 1 84

514

195 7 1 1 29 387 216
196 7 2 1 187

197 7 2 1 77 545

198 5 1 2 104 0 399
199 5 1 1 85 838 834
200 5 1 1 47 51 104

Geog. Region	Control	Service	Census	Births	Personnel
792
1762
2310
		100
	159	3810
742
			173	1594
		169
430
2049	676
2648
2450
		146	755
1993
2275
1		494	1091
1313	671
753
1	583		607
2017	929
995
2045	408
1686	1251
503
	202	2047
1412	1343
461	1517	1723
529
414	2719	3694
1074	1042
1421
1	525
3			194	1983
1442	1653
1107
	298	841
	1064
759	605
1317
1751		1165
568
507
	714	479
2243	1456
378	3	966	3486
1308			885
2514	1001
3714		330
337
1	193
	132		1	161
1217	1224
2641	1704
	815
520	712
1168	1769
875
1618
472
297
1284	847
418	2154	3928
1231
806	663
820
3968	2581
1298
3655	2534
864
3063
827	973
570	439
1849
		127	549
611
1471
575
1275	1916
516	5699	2620
1364	571
703
	160
779	1330
370
340	2202	3123
3346	2745
	576
808
728
923	2462	4087
3311	3012
4207	3090
148	416	1358
1143	2312
1124
1026	1779
338
453
609
562	647
2074
2122	2232
948
409
710	741
1625
538
956
637
	1227	2256
963	731
3038	1477
868	939
1189
2849	3516
1728
188
630
2993	1379
296	1108
1964
	601
	1946	1593
1055

Option 3 – Consumer Food

1 1

1 1

1 1

1 1

1 1

1 1

1 1

1 1

1 1

1 1

8

7

1 1

1 1

3805

1 1

1 1

1 1

1 1

1 1

1 1

1 1

1 1

1 1

5

1 1

1 1

1 1

1 1

1 1

1 1

1 1

2

0 1 1

1 1

1 1

4

1 1

0 1 1

1 1

1 1

1 1

1 1

1 1

1 1

1 1

1 2

1 2

1 2

1 2

1 2

1 2

1 2

1 2

1 2

1 2

1 2

1 2

1 2

1 2

1 2

1 2

1 2

1 2

1 2

1 2

9551

2 1

2 1

2 1

2 1

2 1

0 2 1

2 1

2 1

2 1

2 1

7204

2 1

2 1

3

2 1

0 2 1

7761

2 1

2 1

2 1

936 2 1

2 1

2 1

3

2 1

2 1

2 1

2 1

2 1

2 1

2 1

9

2 1

2 1

2 1

6

2 2

146 2 2

2 2

2 2

2 2

2 2

2 2

2 2

2 2

2 2

2 2

2 2

2 2

2 2

2 2

3 1

3 1

3 1

9522

3 1

3 1

3 1

3 1

3 1

3 1

817 3 1

3 1

3 1

3681

3 1

3 1

3 1

6314

3 1

3 1

3 1

9093

3 1

3 1

3 2

3 2

9286

3 2

1

3 2

3 2

3 2

3 2

3 2

3 2

3 2

5188

3 2

3 2

3 2

3 2

3 2

240 3 2

0 3 2

3 2

3 2

0 3 2

4 1

4 1

4 1

4 1

4 1

4 1

8185

4 1

4 1

4 1

4 1

4 1

4 1

7436

4 1

4 1

4 1

4 1

4 1

4 1

4 1

4 1

4 1

4 1

4 1

9

4 1

4 1

4 1

6426

4 1

5839 4 1

4 1

4 1

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

4 2

9908

4 2

Annual Food Spending ($)	Annual Household Income ($)	Non mortgage household debt ($)	Region: 1 = NE 2 = MW 3 = S 4 = W	Location: 1 = Metro 2 = Outside Metro
8909	56697	23180
5684	35945	7052
10706	52687	16149
14112	74041	21839
13855	63182	18866
15619	79064	21899
2694	25981	8774
9127	57424	15766
13514	72045	27685
	6314	38046	8545
7622	5	240		2805
4322	41405	6998
29684	4806
6674	49246	13592
7347	41491	4088
2911	26703	15876
8026	48753	16714
8567	55555	16783
10345	71483	21407
8694	50	980	19114
8821	46403	7817
8678	51927		1441
14331	84769	17295
9619	59062	16687
	9286	57952	14161
8206	58355	19538
16408	81694	15187
12757	6	9522	14651
17740	9	613
7739	57796	22057
15383	88276	1896
4579		3226	7979
11679	65928
12877	69924	27330
16232	91108	9876
9621	54070	1	9908
8171	47238	17819
12128	77427	31340
8642	59805	4963
12400	60334	6632
9185	54114	18593
7862	40680	15202
9	775	58263	1486
6771	52008	21	713
3059	39643	12179
13211	70309	13221
7408	46450	5602
11581	76140	33874
14233	80833	11478
3352	31899	2762
2630	21647	2663
	9093	65924	11355
12652	65923	5132
9559	62811	12613
6112	42335	3149
10431	65134	15196
12630	64621	21433
4578	36553	5502
	9551	62910	11376
	10262	70727	13287
57634	11857
10143	56549	16136
8955	59662	11627
10197	57350	18432
11234	56447	10871
9320	61136
9089	51526	4902
1	2300	79979	17270
11484	66733	15145
11215	75359	15611
40795	8975
5579	39128	6576
	1172	75482	12508
9353	63998
45845	6671
4261	38223	8576
9830	66787	1178
12386	77852
8673	55825	14167
10944	57022	9018
9910		6426	12768
9928	75881	17423
4264	34343	21323
7971	41243	21009
8290	53021	20151
12669	66991	9250
7272	49719	20838
9784		5839	16065
9187	50477	9407
5866	39112	20409
9456		5188	11668
6270	34797
9518	62348	5201
10968	78704	17002
8865	53620	32004
9226	51577	15922
4913	34761	17704
6976	60968	17799
8152	51281	8167
2887	25013	18763
8062	59238	10815
8895	47344	11814
8444	52645	22469
6148	35309	17139
4563	34355	10612
	8185	50630	21187
3391	29056	15735
	7436	48721	18363
50459	16478
11290	72805	21238
10403	56954	22218
4693	39343	24696
5626	38833	14371
11869	55021	35576
13055	77605
8783	57937	18591
13031	63343	25531
36479	17950
5549	40381	14257
4108	26309	26581
41421	22470
7700	54579	29065
7479	40551	31757
50369	6404
9863	54422	24334
8043	51836	26213
9552	73600	36374
51873	29631
7987	48003		1726
3875	36519	13579
10746	75152	10659
6888	44974	23711
5479	48923	4594
6949	43769	21221
10650	75947	33357
41423	33641
5311	40189	17791
4691	36772	5829
8056	59690	19594
11304	53654	23066
8112	59067
8696	65962
5869	37254	10157
3776	33568	14143
11829	56934
13087	88822	17565
10986	59635	27863
5762	38407	18867
11617	78627	11894
9895	47710	22930
16293	64443	31687
58871	35424
13	972	87954	11549
11243	54778	12552
4635	39825	19494
10063	49536	12195
8426	60102	13787
49139	22356
11747	51052	4553
15397	70500	12025
6842	54894	16217
9678	60570	4106
12852	57625	31228
10114	56956	25907
8496	61400	1093
6689	50532	17106
15696	72774	17793
9841	69981	21607
	1252	66891	17689
10210	67431	19995
8868	64782	14489
38987	17864
11096	64867
10086	50421	8689
	2587	27076	17534
12492	51784	20284
8456	54135	22037
6801	53291	23342
6339	49804	34943
7802	52205	28579
9717	72841	22349
6026	46238	20165
5618	45938	10538
10217	77716	18516
8338	59711	7980
9048	42106	19786
4017	36462	9935
10906	53403	18177
15148	71290	6696
8830	66759	20972
8481	57616	28767
11358	76221	1373
10553	78202	5920
6969	55164	24795
	13219	61171	21482
3543	34093	25969
7326	50647	10750
8458	59898	22940
11766	52884	25970
73629	7112

Option 4 – Financial

6

2

4

2.08

3

19

6

6

7

0.2

0.6

6

7.1

9

1.4

4

7

6

1.4

2

4

7

1.1

7

25322

3.8

5 1916

6

9

5

5

1

775

3 954 2299 2.4

7

6

8

1.1

3

1.8

1

18

1

0.16

3

2

7.7

3

1.2

2

2.08

2

16

3

12.3

1

2

6.1 0.33

3

1.8

6

12.3

0.2

7 1064 1067

7

3

12.2

1

1.3 0.2 22

2 966 613 228

4

7.9 1.18

4 10262

6.9

2

3 179 326 8.3

14

4 3226 1227

1.4

2

47

1

3

8 1.66 1.5 13.7

6 2578

14.5

1.04 13.3

5

24.1

6 660

1

10.6

26.7

6

0.4

2

19

1.01 14.5

7

0.9

5

16

1

1.2 16.2

5

11 1.59 0.5 26

1

2178 7.9

1.19 16.3

5

26.6

2

12.6 2.47 0.6 8.3

7

2

7.2 0.48 0.5

2

2805 12.3

0.36

7

12.3

18

5 555 848

0.16 22.4

2

1441 25 2.1 1

3 799

1.6

3 2587

0.9 14.4

2

1946

3 1.2 14.5

6

16

0.6

5

713 9.4 0.6

3

5.2

1

7.7 2.1

7

1 17.1

5

27.9 1.7 0.68

5

5.8

1.08

7

6 129 194

1.48 0.35 16.3

2

3

11.4 1.8

12

6

17

2

2.13

13.4

4 2300 885

0.32 17

5

1

17.4

5 980

20.5

7

18 2.7 1 13

6

14.7 1.8 0.3

7

11.5

1

1.75

6

2.39

14.2

7

0.24 23

6

0.4 17

7

9.5

1.1

2

0.79

5 1172 1726 7.5

0.16 29

7 6064

0.8

6

17

7

12.6

19.8

7

9.6

17.2

5

1.04

4 1819 972 9.2

2

4.8

0.35 20.5

4 13219

27.2

2 2187

14

6 601 1252 7.8 1.57 1 17

Company	Type	Total Revenues	Total Assets	Return on Equity	Earnings per Share	Dividends per Share	Average P/E Ratio
AFLAC	7251	29454	1	7.1			2.08				0.2		1	1.5
Albertson’s	14690	5219	2			1.4					0.6
Allstate	20106	80918	20.1	3.56			0.3		10.6
Amerada Hess	8340	7935	0.08	69		8.3
American General	3362	80620			2.1	2			1.2
American Stores	19139	8536		12.2		1.01	0.34	23.5
Amoco	36287	32489	16.7		2.7	1	6.1
Arco Chemical	3995	4116	6.2				1.1	2.8	4		0.4
Ashland	14319	7777		9.5		3.8	1	2.4
Atlantic Richfield	19272	2				1.8	5.41	2.83
Bausch & Lomb	2773		0.8			1.04	2.6
Baxter International	6138	8707	11.5	1.06	1.13	4	7.2
Bristol-Myers Squibb	16701	14977	44.4	3.14	1.52		24.1
Burlington Coat	1777					12.3		1.18	0.02	12.9
Central Maine Power				0.16			0.9	7	9.6
Chevron	41950	35473	18.6	4.95	2.28	1	5.2
CIGNA	14935	108199		13.7		4.8		11.4
Cinergy	4353	8858		13.3		1.59		22.4
Dayton Hudson	27757	14191		1.7		0.33		16.2
Dillard’s	6817	5592		9.2	2.31	15.7
Dominion Resources	7678	20193			7.9	2.15	2.58	1		7.7
Dow Chemical	20018	24040	23.6	3.24	1	1.6
DPL	1356	3585	13.9	0.91	14.3
E. I. DuPont DeNemours	46653	42942	2	1.3	1.23		27.9
Eastman Chemical	4678	5778			16.3	3.63	1.76
Edison International	9235	25101	1.73	13.6
Engelhard	3631	2586	0.38	61.8
Entergy	9562	27001	4.2	1.03	25.4
Equitable	9666	151438	2.86		13.4
Ethyl	53.6	0.71			0.5			12.6
Exxon	137242	9	6064	1	9.4	3.37	1.63		17.1
FPL Group	6369	12449	3.57	1.92		14.4
The GAP	6508	3338	33.7
Georgia Gulf		2.39		0.32	11.8
GIANT Food	4231	1522	0.78	2	6.9
A & P	2995		1.66			0.35	1	7.8
Great Lakes Chemicals	1311	2270	5.5		1.19	0.62	40.5
Green Mountain Power Company		1.57	1.61
Hannaford Bros.	9.9	0.54		26.6
Hercules	1866	2411	3.18				14.5
Houston Industries	6873	18415
Jefferson-Pilot	23131	3.47
Johnson & Johnson	22629	21453		26.7	2.41	0.85
Liberty	3185	11.1	3.34	0.77	12.7
The Limited	9189	4301		0.79		0.48
Lincoln National	4899	77175	0.21	1.96	300.2
Lubrizol	1674	1462	2.66
Lyondell Petrochemical	3010	1559	46.2	3.58	6.4
Mallinkrodt	1868	2988	14.8		2.47	0.66
May Department Stores	12685	9930			20.5	3.11
McKesson	20857	5608
Mercantile Stores	3144	3.53
Merck	23637	25812	36.6	3.74	1.69
Millennium Chemicals	3048	4326
Mobil	65906	43559	16.8	4.01	2.12		17.2
Monsanto	7514	10774	90.7
Morton	2388		1.48	25.2
Murphy Oil	2138	2238	2.94	1.35
Mylan Laboratories	13.5	0.82
NALCO Chemical	1434	18.3
Nevada Power	2339	10.1	1.65		14.2
NIPSCO	4937	14.1	1.53
Olin	2410		17.4
Orion Capital	1591	3884	4.15	9.8
Owens & Minor	3117	0.18	21.7
Pacific Corporation	6278	13880		0.68		1.08	34.2
J. C. Penney	30546	23493		2.13	26.9
Pennzoil	2654	4406	1	5.8	3.76
Pfizer	12504	15336	35.4
Pharmacia & Upjohn	6710	10380	0.61	56.2
Phillips Petroleum	15424	13860	19.9	3.61	1.34	12.4
Poe & Brown	25.1
PPG	7379	6868	28.5	3.94	1.33		14.7
PP&L Resources	3049	9485	1.67
Progressive	4190	7560	18.7	5.31		0.24
Rohm & Haas	3999	3900		19.8	0.63
Ruddick	12.5	1.02
Schering-Plough	6778	6507	51.2	1.95	0.74	24.6
Sears, Roebuck	41296	38700	20.3	2.99	0.92
Stryker	985	1.28	0.11		27.2
Sun	10531	4667
Sunamerica	2114	35637	19.5
Texaco	46667	29600	20.9	4.87		1.75
The TJX Companies	7389	2610	26.3	0.09	8.2
Torchmark	2283	10967	1	7.5	0.59
Tosco	13282	5975	10.9	1.37
Travelers	37609	386555	14.9	2.54
Ultramar Diamond Shamrock	10882	5595	1.94	16.1
Union Carbide	6502	6964	28.8	4.53	10.7
United States Surgical Corporation	1.21
UNOCAL	7530	28.9	2.65	15.5
UNUM	4077	13200	15.2	2.59	0.56
USX-Marathon	15754	10565	1.58	0.76
Valero Energy	5756	2493	2.03	0.42
Warner-Lambert	8180	8031	30.7	0.51	35.7
WEIS Markets	1.87	0.94	16.9
Wellman	1083	1319	0.97
Winn-Dixie Stores	2921	15.3	1.36	0.98
WITCO	2298	1.55	1.12	24.9
Zenith Nation Insurance

Title ABC/ 1 23 Version X

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Week 6 Options QNT/561 Version 9

University of Phoenix Material

Option 1: Manufacturing Database

This database contains six variables taken from 20 industries and 140 subindustries in the United States. Some of the industries are food products, textile mill products, furniture, chemicals, rubber products, primary metals, industrial machinery, and transportation equipment. The six variables are Number of Employees, Number of Production Workers, Value Added by Manufacture, Cost of Materials, End-of-Year Inventories, and Industry Group. Two variables, Number of Employees and Number of Production Workers, are in units of 1000. Three variables, Value Added by Manufacture, Cost of Materials, and End-of-Year Inventories, are in million-dollar units. The Industry Group variable consists of numbers from 1 to 20 to denote the industry group to which the particular subindustry belongs.

Option 2: Hospital Database

This database contains observations for six variables on U.S. hospitals. These variables include Geographic Region, Control, Service, Census, Number of Births, and Personnel.

The region variable is coded from 1 to 7, and the numbers represent the following regions:

1 = South

2 = Northeast

3 = Midwest

4 = Southwest

5 = Rocky Mountain

6 = California

7 = Northwest

Control is a type of ownership. Four categories of control are included in the database:

1 = government, nonfederal

2 = nongovernment, not-for-profit

3 = for-profit

4 = federal government

Service is the type of hospital. The two types of hospitals used in this database are:

1 = general medical

2 = psychiatric

Option 3: Consumer Food

The consumer food database contains five variables: Annual Food Spending per Household, Annual Household Income, Non-Mortgage Household Debt, Geographic Region of the U.S. of the Household, and Household Location. There are 200 entries for each variable in this database representing 200 different households from various regions and locations in the United States. Annual Food Spending per Household, Annual Household Income, and Non-Mortgage Household Debt are all given in dollars. The variable Region tells in which one of four regions the household resides. In this variable, the Northeast is coded as 1, the Midwest is coded 2, the South is coded as 3, and the West is coded as 4. The variable Location is coded as 1 if the household is in a metropolitan area and 2 if the household is outside a metro area. The data in this database were randomly derived and developed based on actual national norms.

Option 4: Financial Database

The financial database contains observations on seven variables for 100 companies. The variables are Type of Industry, Total Revenues ($ millions), Total Assets ($ millions), Return on Equity (%), Earnings per Share ($), Dividends per Share ($), and Average Price per Earnings (P/E) ratio. The companies represent seven different types of industries. The variable Type displays a company’s industry type as:

1 = apparel

2 = chemical

3 = electric power

4 = grocery

5 = healthcare products

6 = insurance

7 = petroleum

Copyright © XXXX by University of Phoenix. All rights reserved.

Copyright © 2017 by University of Phoenix. All rights reserved.

Title ABC/ 1 23 Version X

1

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Part 3 Inferential Statistics QNT/561 Version 9

Part 3: Inferential Statistics

Option 1: Manufacturing Database

1. The National Association of Manufacturers (NAM) contracts with your consulting company to determine the estimate of mean number of production workers. Construct a 95% confidence interval for the population mean number of production workers. What is the point estimate? How much is the margin of error in the estimate?

2. Suppose the average number of employees per industry group in the manufacturing database is believed to be less than 150 (1000s). Test this belief as the alternative hypothesis by using the 140 SIC Code industries given in the database as the sample. Let α = .10. Assume that the number of employees per industry group are normally distributed in the population.

3. You are also required to determine whether there is a significant difference between mean Value Added by the Manufacturer and the mean Cost of Materials in manufacturing using alpha of 0.01.

4. You are requested to determine whether there is a significantly greater variance among values of Cost of Materials than of End-of-Year Inventories.

Option 2: Hospital Database

1. As a consultant, you need to use the Hospital database and construct a 90% confidence interval to estimate the average census for hospitals. Change the level of confidence to 99%. What happened to the interval? Did the point estimate change?

2. Determine the sample proportion of the Hospital database under the variable “service” that are “general medical” (category 1). From this statistic, construct a 95% confidence interval to estimate the population proportion of hospitals that are “general medical.” What is the point estimate? How much error is there in the interval?

3. Suppose you want to “prove” that the average hospital in the United States averages more than 700 births per year. Use the hospital database as your sample and test this hypothesis. Let alpha be 0.01.

4. On average, do hospitals in the United States employ fewer than 900 personnel? Use the hospital database as your sample and an alpha of 0.10 to test this figure as the alternative hypothesis. Assume that the number of births and number of employees in the hospitals are normally distributed in the population.

Option 3: Consumer Food

1. Suppose you want to test to determine if the average annual food spending for a household in the Midwest region of the U.S. is more than $8,000. Use the Midwest region data and a 1% level of significance to test this hypothesis. Assume that annual food spending is normally distributed in the population.

2. Test to determine if there is a significant difference between households in a metro area and households outside metro areas in annual food spending. Let α = 0.01.

3. The Consumer Food database contains data on Annual Food Spending, Annual Household Income, and Non-Mortgage Household Debt broken down by Region and Location. Using Region as an independent variable with four classification levels (four regions of the U.S.), perform three different one-way ANOVA‘s—one for each of the three dependent variables (Annual Food Spending, Annual Household Income, Non-Mortgage Household Debt). Did you find any significant differences by region?

Option 4: Financial Database

1. Use this database as a sample and estimate the earnings per share for all corporations from these data. Select several levels of confidence and compare the results.

2. Are the average earnings per share for companies in the stock market less than $2.50? Use the sample of companies represented by this database to test that hypothesis. Let α = .05.

3. Test to determine whether the average return on equity for all companies is equal to 21. Use this database as the sample and α = .10. Assume that the earnings per share and return on equity are normally distributed in the population.

4. Do various financial indicators differ significantly according to type of company? Use a one-way ANOVA and the financial database to answer this question. Let Type of Company be the independent variable with seven levels (Apparel, Chemical, Electric Power, Grocery, Healthcare Products, Insurance, and Petroleum). Compute three one-way ANOVAs, one for each of the following dependent variables: Earnings Per Share, Dividends Per Share, and Average P/E Ratio.

Copyright © XXXX by University of Phoenix. All rights reserved.

Copyright © 2017 by University of Phoenix. All rights reserved.