# Signature Assignment

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Assignment Content

Purpose of Assignment

The purpose of this assignment is for students to synthesize the concepts learned throughout the course. This assignment will provide students an opportunity to build critical thinking skills, develop businesses and organizations, and solve problems requiring data by compiling all pertinent information into one report.

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 1,650-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 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.

Plagiarism Free

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

68

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 1

1

 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

 Title ABC/ 1 23 Version X 1

1

 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

 Title ABC/ 1 2 3 Version X 1
 Part 3 Inferential Statistics QNT/561 Version 9 2

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.