Provide a 1,600-word detailed, four part, statistical report with the following sections:
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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:
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.
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| 1 |
– Manufacturer
| SIC Code |
No. Emp. |
No. Prod. Wkrs. |
Value Added by Mfg. |
Cost of Materials |
End Yr. Inven. |
Indus. Grp. |
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|
|
| 111 |
30
2
|
| 354 |
9
289 76 45
| 1
|
|
| 154 |
7
1
|
| 308 |
5
2749 |
9
291 67 43
| 2
| 600 |
6
1
|
|
|
|
| 328 |
80
|
|
| 107 |
18
10
295 25 18
|
| 346 |
4
6182 |
6
| 58 |
10
| 299 |
14 8
| 2187 |
4446 |
| 670 |
10
| 301 |
65 54
7079 |
7091 |
| 1067 |
11
|
| 302 |
8 7
442 |
|
| 496 |
|
| 175 |
11
| 305 |
61 46
4528 |
| 3805 |
| 105 |
7
11
306 122 95
| 7275 |
7
| 195 |
|
|
| 141 |
1
11
308
| 763 |
598
|
| 556 |
21
57264 |
| 118 |
74
11
| 311 |
15 12
|
|
| 131 |
3
1865 |
404 12
313 3 2
|
|
| 162 |
163 35 12
314 37 31
|
|
| 190 |
7
|
|
| 168 |
2
716 12
|
| 315 |
2 2 53 85 62 12
|
|
| 316 |
6 4
747 |
|
|
| 395 |
199 12
| 317 |
8 7 328
255 |
75 12
|
| 319 |
7 6 233
|
| 177 |
40 12
321 12 9
|
|
|
| 171 |
7
| 943 |
282 13
|
| 322 |
60 51
6532 |
|
|
|
| 352 |
7
1
| 505 |
13
| 323 |
64 50
4850 |
4254 |
883 |
13
| 324 |
17 13
3
| 509 |
2282 |
828 |
13
325 31 25
| 2
| 176 |
|
|
| 138 |
7
700 |
13
|
| 326 |
45 36
2696 |
1
| 183 |
600 13
327 205
|
| 152 |
|
|
| 157 |
39
1
| 701 |
0
| 196 |
6
13
328 17 13
| 999 |
565 |
263 13
329 72 53
| 7
| 838 |
5
|
| 432 |
1652 |
13
| 331 |
221
|
| 174 |
29180 |
| 456 |
96
12
|
|
| 198 |
14
| 332 |
| 128 |
106
9061 |
6913 |
| 1543 |
14
| 333 |
35 26
4200 |
11
|
| 184 |
1
| 834 |
14
| 334 |
15 11
1410 |
5
| 735 |
694 14
|
| 335 |
162 123
| 166 |
70
3
|
| 189 |
2
6
|
| 377 |
14
|
| 336 |
94 79
5856 |
4696 |
938 |
14
|
|
|
| 339 |
32 23
3
| 164 |
2
|
| 790 |
800 |
14
| 341 |
33 27
|
| 399 |
9
9
|
| 364 |
|
|
| 145 |
3
15
|
| 342 |
140 107
|
| 117 |
50
8720 |
| 312 |
4
15
| 343 |
45 32
4
| 412 |
3527
1121 |
15
| 344 |
432 315
2
| 797 |
4
31527 |
| 7204 |
15
345 104 81
| 6
| 936 |
4909 |
1768 |
15
346 259
|
|
| 211 |
19880 |
| 215 |
31
3
| 997 |
15
|
|
| 347 |
129 99
7
|
| 793 |
6232 |
1181 |
15
| 348 |
40 24
3528 |
| 1689 |
|
| 1077 |
15
| 349 |
300 219
| 2171 |
8
19273 |
6460 |
15
| 351 |
79 55
10513 |
12
|
| 954 |
| 367 |
9
16
352 94 70
| 9
|
| 545 |
1
|
|
|
| 185 |
8
3339 |
16
353 205
|
| 133 |
1
| 817 |
8
23474 |
7344 |
16
354 295 211
| 22673 |
| 143 |
43
6730 |
16
355 192 110
| 1
| 922 |
1
16515 |
6823 |
16
356 265 172
| 23110 |
18543 |
| 789 |
8
16
| 357 |
259 96
4
|
|
| 113 |
5
60857 |
|
|
|
| 102 |
7
7
16
| 358 |
201 147
17521 |
2
| 1819 |
4857 |
16
| 359 |
| 392 |
293 |
| 25322 |
13897 |
4964 |
16
| 361 |
74 51
6700 |
5
| 523 |
| 149 |
5
17
|
| 362 |
171 120
14278 |
12657 |
3887 |
17
| 363 |
|
|
|
| 108 |
87
9
| 466 |
1
| 2578 |
| 2299 |
17
364 157 117
|
| 134 |
28
|
| 1106 |
5
3076 |
17
| 365 |
49 37
3459 |
| 762 |
1
1070 |
17
| 366 |
258 |
120
38705 |
2
| 959 |
1
9467 |
17
367
| 588 |
368 |
84059 |
44486 |
13145 |
17
| 369 |
| 151 |
106
|
| 139 |
20
13
| 398 |
3
| 514 |
17
| 371 |
772 |
634
10
| 589 |
9
223639 |
| 158 |
52
18
| 372 |
377 190
45220 |
42367 |
| 3681 |
4
18
373 141 108
| 7903 |
| 776 |
0
2165 |
18
| 374 |
31 23
2590 |
4363 |
1233 |
18
|
| 375 |
18 14
1435 |
|
| 167 |
4
412 18
|
| 376 |
81 29
9986 |
8120 |
4770 |
18
| 379 |
47 35
3564 |
5476 |
1102 |
18
381 186 68
| 2
|
| 1071 |
8760 |
6183 |
19
|
| 382 |
272 141
29028 |
18028 |
7681 |
19
384
| 268 |
157
| 310 |
51
16787 |
| 7761 |
19
| 385 |
27 17
2
| 390 |
1020 |
426 |
19
|
| 386 |
61 36
14032 |
8
|
| 114 |
2290 19
387 6 4
|
|
|
| 415 |
382 177 19
| 391 |
43 30
2761 |
3646 |
1
|
| 451 |
20
| 393 |
13 10
685 |
506 328 20
| 394 |
| 103 |
76
8327 |
| 660 |
4
2608 |
20
395 35 26
| 2643 |
1789 |
| 799 |
20
|
| 396 |
24 19
1406 |
997 415 20
399 179 123
| 1
|
| 119 |
9
8530 |
2861 |
20
Option 2 –
Hospital |
Hospital
| Geog. Region |
Control |
Service |
Census |
Births |
Personnel |
1 1 2 1 107 312
| 792 |
2 1 1 1 198 1077
| 1762 |
3 1 2 1 356 1027
| 2310 |
4 1 1 1
|
|
| 100 |
355 328
5 7 1 1 9 168 181
6 4 2 1
|
| 159 |
3810 |
1077
7 4 4 1 65 735
| 742 |
8 4 2 1 48 1 131
9 1 2 1 253
|
|
|
| 173 |
3
1594 |
10 1 1 1 21 257 233
11 1 1 1 27
|
|
| 169 |
241
12 6 3 1 30
| 430 |
203
13 6 3 1 43 0 325
14 6 2 1 233
| 2049 |
676 |
15 6 4 1 2 211 347
16 6 1 1 11 16 79
17 6 3 1 84
| 2648 |
505
18 6 2 1 219
| 2450 |
1543
19 6 3 1 112
|
|
| 146 |
5
755 |
20 6 3 1 124 0 959
21 6 3 1 50
| 1993 |
325
22 6 2 1 142
| 2275 |
954
23 6 2 1 111
| 1
|
| 494 |
1091 |
24 6 1 1 140
| 1313 |
671 |
25 6 3 1 28 451 300
26 6 2 1 154 1689
| 753 |
27 6 2 1 150
| 1
| 583 |
| 607 |
28 6 3 1 144
| 2017 |
929 |
29 6 3 1 42
| 995 |
354
30 6 2 1 77
| 2045 |
408 |
31 5 2 1 119
| 1686 |
1251 |
32 5 2 1 27
| 503 |
386
33 5 2 1 15 126 144
34 2 2 1 179
|
| 202 |
6
2047 |
35 2 2 1 175
| 1412 |
1343 |
36 2 2 1
| 461 |
1517 |
1723 |
37 1 3 2 32 0 96
38 1 2 1 74 0
| 529 |
39 1 1 1
| 414 |
2719 |
3694 |
40 1 2 1 253
| 1074 |
1042 |
41 1 3 1 180
| 1421 |
1071
42 1 1 1 184 762
| 1
| 525 |
43 1 2 1 243
| 3
|
|
| 194 |
1983 |
44 1 2 1 115 496 670
45 1 1 1 215
| 1442 |
1653 |
46 1 3 2 48 0 167
47 1 3 1 124
| 1107 |
793
48 1 2 1 189
|
| 298 |
9
841 |
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
|
| 1064 |
943
54 1 2 1 154
| 759 |
605 |
55 7 2 1 76
| 1317 |
596
56 7 3 1 165
| 1751 |
| 1165 |
57 3 1 2 295 0
| 568 |
58 3 3 1 101 0
| 507 |
59 3 2 1 69
|
| 714 |
479 |
60 3 2 1 12 99 136
61 3 2 1 185
| 2243 |
1456 |
62 3 2 1
| 378 |
3
| 966 |
3486 |
63 3 2 1 114
| 1308 |
|
| 885 |
64 3 3 2 49 0 243
65 3 2 1 106
| 2514 |
1001 |
66 3 2 1 460
| 3714 |
| 330 |
1
67 3 2 1 43 126
| 337 |
68 3 4 1 29 556
| 1
| 193 |
69 3 2 1 125
|
| 132 |
7
| 1
| 161 |
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
| 1217 |
1224 |
74 3 2 1 207
| 2641 |
1704 |
75 3 2 1 223 790
|
| 815 |
76 3 2 1 82
| 520 |
712 |
77 3 1 1 64 35 156
78 3 2 1 139
| 1168 |
1769 |
79 3 2 1 109 793
| 875 |
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
| 1618 |
86 1 1 1 47 0 244
87 1 2 1 80 776 525
88 1 3 1 50 451
| 472 |
89 1 3 2 113 0 94
90 1 2 1 45 145
| 297 |
91 1 1 1 76
| 1284 |
847 |
92 1 3 1 129 1 234
93 2 2 1 60 319 401
94 2 2 1
| 418 |
2154 |
3928 |
95 2 2 1 17 295 198
96 2 2 1 138 496
| 1231 |
97 2 2 1 64 589 545
98 2 2 1 62
| 806 |
663 |
99 2 1 1 131 701
| 820 |
100 2 2 1 265
| 3968 |
2581 |
101 3 4 2 456 0
| 1298 |
102 3 2 2 40 0 126
103 3 1 1 310
| 3655 |
2534 |
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
| 864 |
108 3 3 2 15 0 66
109 3 2 1 78
| 3063 |
556
110 3 1 1 123 169 347
111 3 2 1 54 66 239
112 3 2 1 96
| 827 |
973 |
113 1 3 1 82
| 570 |
439 |
114 1 1 2 1106 0
| 1849 |
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
|
|
| 127 |
494
549 |
119 3 1 2 180 0
| 611 |
120 3 4 1 59 0 330
121 5 2 1 127 0
| 1471 |
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
| 575 |
127 3 2 1 172
| 1275 |
1916 |
128 5 3 1
| 516 |
5699 |
2620 |
129 5 3 1 120
| 1364 |
571 |
130 5 1 1 179 714
| 703 |
131 2 4 1 140 0 535
132 2 3 2 78 0
|
| 160 |
133 2 3 2 68 0 202
134 2 2 1 186
| 779 |
1330 |
135 2 2 1 91 0
| 370 |
136 2 1 1
| 340 |
2202 |
3123 |
137 5 1 1 254
| 3346 |
2745 |
138 5 2 1 108 1071 815
139 5 2 1 61 352
|
| 576 |
140 2 2 1 174 254 502
141 2 1 2 306 0
| 808 |
142 2 3 2 28 0 50
143 2 2 1 395 699
| 728 |
144 2 2 1
| 923 |
2462 |
4087 |
145 2 1 1 335
| 3311 |
3012 |
146 1 2 1 46 0 68
147 1 1 1 316
| 4207 |
3090 |
| 148 |
1 4 2
416 |
0
1358 |
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
| 1143 |
2312 |
153 3 4 2 232 0
| 1124 |
154 3 1 2 138 0 336
155 3 2 1 38 509 415
156 3 2 1 245
| 1026 |
1779 |
157 3 1 2 171 0
| 338 |
158 3 3 1 51 447
| 453 |
159 3 4 1 28 1161 437
160 3 1 2 797 0 261
161 7 2 1 56 922
| 609 |
162 7 1 1 69
| 562 |
647 |
163 7 1 1 40 78 61
164 7 4 1 163 0
| 2074 |
165 7 2 1 231
| 2122 |
2232 |
166 2 1 2 523 0
| 948 |
167 2 4 1 31 0
| 409 |
168 2 2 2 43 0 153
169 2 2 1 66
| 710 |
741 |
170 2 2 1 231 1165
| 1625 |
171 1 4 1 11 466
| 538 |
172 1 3 1 144 1106 789
173 1 3 1 43 376 395
174 3 4 1 185 0
| 956 |
175 3 2 1 82
| 637 |
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
|
| 1227 |
2256 |
180 1 3 1 93
| 963 |
731 |
181 1 4 1 86
| 3038 |
1477 |
182 1 3 2 28 0 102
183 1 3 2 25 0 106
184 1 3 1 181
| 868 |
939 |
185 5 3 1 39
| 1189 |
392
186 5 1 1 302
| 2849 |
3516 |
187 5 2 1 80
| 1728 |
785
| 188 |
5 2 1 63 2171 607
189 2 2 1 31 364 273
190 2 2 2 170 0
| 630 |
191 1 1 1 203
| 2993 |
1379 |
192 1 2 1
| 296 |
0
1108 |
193 1 3 1 83
| 1964 |
583
194 7 2 1 84
|
| 601 |
514
195 7 1 1 29 387 216
196 7 2 1 187
|
| 1946 |
1593 |
197 7 2 1 77 545
| 1055 |
198 5 1 2 104 0 399
199 5 1 1 85 838 834
200 5 1 1 47 51 104
Option 3 – Consumer Food
| 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 |
1 1
| 5684 |
35945 |
7052 |
1 1
| 10706 |
52687 |
16149 |
1 1
| 14112 |
74041 |
21839 |
1 1
| 13855 |
63182 |
18866 |
1 1
| 15619 |
79064 |
21899 |
1 1
| 2694 |
25981 |
8774 |
1 1
| 9127 |
57424 |
15766 |
1 1
| 13514 |
72045 |
27685 |
1 1
|
| 6314 |
38046 |
8545 |
1 1
| 7622 |
5
| 240 |
8
| 2805 |
7
1 1
| 4322 |
41405 |
6998 |
1 1
3805
| 29684 |
4806 |
1 1
| 6674 |
49246 |
13592 |
1 1
| 7347 |
41491 |
4088 |
1 1
| 2911 |
26703 |
15876 |
1 1
| 8026 |
48753 |
16714 |
1 1
| 8567 |
55555 |
16783 |
1 1
| 10345 |
71483 |
21407 |
1 1
| 8694 |
50
| 980 |
19114 |
1 1
| 8821 |
46403 |
7817 |
1 1
| 8678 |
51927 |
| 1441 |
5
1 1
| 14331 |
84769 |
17295 |
1 1
| 9619 |
59062 |
16687 |
1 1
|
| 9286 |
57952 |
14161 |
1 1
| 8206 |
58355 |
19538 |
1 1
| 16408 |
81694 |
15187 |
1 1
| 12757 |
6
| 9522 |
14651 |
1 1
| 17740 |
9
| 613 |
2
0 1 1
| 7739 |
57796 |
22057 |
1 1
| 15383 |
88276 |
1896 |
1 1
| 4579 |
| 3226 |
4
7979 |
1 1
| 11679 |
65928 |
0 1 1
| 12877 |
69924 |
27330 |
1 1
| 16232 |
91108 |
9876 |
1 1
| 9621 |
54070 |
1
| 9908 |
1 1
| 8171 |
47238 |
17819 |
1 1
| 12128 |
77427 |
31340 |
1 1
| 8642 |
59805 |
4963 |
1 1
| 12400 |
60334 |
6632 |
1 1
| 9185 |
54114 |
18593 |
1 2
| 7862 |
40680 |
15202 |
1 2
| 9
| 775 |
58263 |
1486 |
1 2
| 6771 |
52008 |
21
| 713 |
1 2
| 3059 |
39643 |
12179 |
1 2
| 13211 |
70309 |
13221 |
1 2
| 7408 |
46450 |
5602 |
1 2
| 11581 |
76140 |
33874 |
1 2
| 14233 |
80833 |
11478 |
1 2
| 3352 |
31899 |
2762 |
1 2
| 2630 |
21647 |
2663 |
1 2
|
| 9093 |
65924 |
11355 |
1 2
| 12652 |
65923 |
5132 |
1 2
| 9559 |
62811 |
12613 |
1 2
| 6112 |
42335 |
3149 |
1 2
| 10431 |
65134 |
15196 |
1 2
| 12630 |
64621 |
21433 |
1 2
| 4578 |
36553 |
5502 |
1 2
|
| 9551 |
62910 |
11376 |
1 2
|
| 10262 |
70727 |
13287 |
1 2
9551
| 57634 |
11857 |
2 1
| 10143 |
56549 |
16136 |
2 1
| 8955 |
59662 |
11627 |
2 1
| 10197 |
57350 |
18432 |
2 1
| 11234 |
56447 |
10871 |
2 1
| 9320 |
61136 |
0 2 1
| 9089 |
51526 |
4902 |
2 1
| 1
| 2300 |
79979 |
17270 |
2 1
| 11484 |
66733 |
15145 |
2 1
| 11215 |
75359 |
15611 |
2 1
7204
| 40795 |
8975 |
2 1
| 5579 |
39128 |
6576 |
2 1
|
| 1172 |
3
75482 |
12508 |
2 1
| 9353 |
63998 |
0 2 1
7761
| 45845 |
6671 |
2 1
| 4261 |
38223 |
8576 |
2 1
| 9830 |
66787 |
1178 |
2 1
| 12386 |
77852 |
936 2 1
| 8673 |
55825 |
14167 |
2 1
| 10944 |
57022 |
9018 |
2 1
| 9910 |
| 6426 |
3
12768 |
2 1
| 9928 |
75881 |
17423 |
2 1
| 4264 |
34343 |
21323 |
2 1
| 7971 |
41243 |
21009 |
2 1
| 8290 |
53021 |
20151 |
2 1
| 12669 |
66991 |
9250 |
2 1
| 7272 |
49719 |
20838 |
2 1
| 9784 |
| 5839 |
9
16065 |
2 1
| 9187 |
50477 |
9407 |
2 1
| 5866 |
39112 |
20409 |
2 1
| 9456 |
| 5188 |
6
11668 |
2 2
| 6270 |
34797 |
146 2 2
| 9518 |
62348 |
5201 |
2 2
| 10968 |
78704 |
17002 |
2 2
| 8865 |
53620 |
32004 |
2 2
| 9226 |
51577 |
15922 |
2 2
| 4913 |
34761 |
17704 |
2 2
| 6976 |
60968 |
17799 |
2 2
| 8152 |
51281 |
8167 |
2 2
| 2887 |
25013 |
18763 |
2 2
| 8062 |
59238 |
10815 |
2 2
| 8895 |
47344 |
11814 |
2 2
| 8444 |
52645 |
22469 |
2 2
| 6148 |
35309 |
17139 |
2 2
| 4563 |
34355 |
10612 |
2 2
|
| 8185 |
50630 |
21187 |
3 1
| 3391 |
29056 |
15735 |
3 1
|
| 7436 |
48721 |
18363 |
3 1
9522
| 50459 |
16478 |
3 1
| 11290 |
72805 |
21238 |
3 1
| 10403 |
56954 |
22218 |
3 1
| 4693 |
39343 |
24696 |
3 1
| 5626 |
38833 |
14371 |
3 1
| 11869 |
55021 |
35576 |
3 1
| 13055 |
77605 |
817 3 1
| 8783 |
57937 |
18591 |
3 1
| 13031 |
63343 |
25531 |
3 1
3681
| 36479 |
17950 |
3 1
| 5549 |
40381 |
14257 |
3 1
| 4108 |
26309 |
26581 |
3 1
6314
| 41421 |
22470 |
3 1
| 7700 |
54579 |
29065 |
3 1
| 7479 |
40551 |
31757 |
3 1
9093
| 50369 |
6404 |
3 1
| 9863 |
54422 |
24334 |
3 1
| 8043 |
51836 |
26213 |
3 2
| 9552 |
73600 |
36374 |
3 2
9286
| 51873 |
29631 |
3 2
| 7987 |
48003 |
| 1726 |
1
3 2
| 3875 |
36519 |
13579 |
3 2
| 10746 |
75152 |
10659 |
3 2
| 6888 |
44974 |
23711 |
3 2
| 5479 |
48923 |
4594 |
3 2
| 6949 |
43769 |
21221 |
3 2
| 10650 |
75947 |
33357 |
3 2
5188
| 41423 |
33641 |
3 2
| 5311 |
40189 |
17791 |
3 2
| 4691 |
36772 |
5829 |
3 2
| 8056 |
59690 |
19594 |
3 2
| 11304 |
53654 |
23066 |
3 2
| 8112 |
59067 |
240 3 2
| 8696 |
65962 |
0 3 2
| 5869 |
37254 |
10157 |
3 2
| 3776 |
33568 |
14143 |
3 2
| 11829 |
56934 |
0 3 2
| 13087 |
88822 |
17565 |
4 1
| 10986 |
59635 |
27863 |
4 1
| 5762 |
38407 |
18867 |
4 1
| 11617 |
78627 |
11894 |
4 1
| 9895 |
47710 |
22930 |
4 1
| 16293 |
64443 |
31687 |
4 1
8185
| 58871 |
35424 |
4 1
| 13
| 972 |
87954 |
11549 |
4 1
| 11243 |
54778 |
12552 |
4 1
| 4635 |
39825 |
19494 |
4 1
| 10063 |
49536 |
12195 |
4 1
| 8426 |
60102 |
13787 |
4 1
7436
| 49139 |
22356 |
4 1
| 11747 |
51052 |
4553 |
4 1
| 15397 |
70500 |
12025 |
4 1
| 6842 |
54894 |
16217 |
4 1
| 9678 |
60570 |
4106 |
4 1
| 12852 |
57625 |
31228 |
4 1
| 10114 |
56956 |
25907 |
4 1
| 8496 |
61400 |
1093 |
4 1
| 6689 |
50532 |
17106 |
4 1
| 15696 |
72774 |
17793 |
4 1
| 9841 |
69981 |
21607 |
4 1
|
| 1252 |
9
66891 |
17689 |
4 1
| 10210 |
67431 |
19995 |
4 1
| 8868 |
64782 |
14489 |
4 1
6426
| 38987 |
17864 |
4 1
| 11096 |
64867 |
5839 4 1
| 10086 |
50421 |
8689 |
4 1
|
| 2587 |
27076 |
17534 |
4 1
| 12492 |
51784 |
20284 |
4 2
| 8456 |
54135 |
22037 |
4 2
| 6801 |
53291 |
23342 |
4 2
| 6339 |
49804 |
34943 |
4 2
| 7802 |
52205 |
28579 |
4 2
| 9717 |
72841 |
22349 |
4 2
| 6026 |
46238 |
20165 |
4 2
| 5618 |
45938 |
10538 |
4 2
| 10217 |
77716 |
18516 |
4 2
| 8338 |
59711 |
7980 |
4 2
| 9048 |
42106 |
19786 |
4 2
| 4017 |
36462 |
9935 |
4 2
| 10906 |
53403 |
18177 |
4 2
| 15148 |
71290 |
6696 |
4 2
| 8830 |
66759 |
20972 |
4 2
| 8481 |
57616 |
28767 |
4 2
| 11358 |
76221 |
1373 |
4 2
| 10553 |
78202 |
5920 |
4 2
| 6969 |
55164 |
24795 |
4 2
|
| 13219 |
61171 |
21482 |
4 2
| 3543 |
34093 |
25969 |
4 2
| 7326 |
50647 |
10750 |
4 2
| 8458 |
59898 |
22940 |
4 2
| 11766 |
52884 |
25970 |
4 2
9908
| 73629 |
7112 |
4 2
Option 4 – Financial
| Company |
Type |
Total Revenues |
Total Assets |
Return on Equity |
Earnings per Share |
Dividends per Share |
Average P/E Ratio |
| AFLAC |
6
7251 |
29454 |
1
| 7.1 |
|
| 2.08 |
|
|
| 0.2 |
2
| 1
| 1.5 |
| Albertson’s |
4
14690 |
5219 |
2
|
|
| 1.4 |
2.08
|
|
|
| 0.6 |
3
19
| Allstate |
6
20106 |
80918 |
20.1 |
3.56 |
|
| 0.3 |
6
| 10.6 |
| Amerada Hess |
7
8340 |
7935 |
0.2
0.08 |
0.6
69
|
| 8.3 |
| American General |
6
3362 |
80620 |
7.1
|
| 2.1 |
9
1.4
2
|
|
| 1.2 |
| American Stores |
4
19139 |
8536 |
| 12.2 |
| 1.01 |
0.34 |
23.5 |
| Amoco |
7
36287 |
32489 |
16.7 |
| 2.7 |
6
1.4
1
| 6.1 |
| Arco Chemical |
2
3995 |
4116 |
6.2 |
|
|
| 1.1 |
4
2.8 |
4
|
| 0.4 |
| Ashland |
7
14319 |
7777 |
| 9.5 |
| 3.8 |
1.1
1
| 2.4 |
| Atlantic Richfield |
7
19272 |
25322
2
|
|
|
| 1.8 |
5.41 |
2.83 |
3.8
| Bausch & Lomb |
5 1916
2773 |
6
| 0.8 |
9
|
| 1.04 |
2.6 |
| Baxter International |
5
6138 |
8707 |
11.5 |
1.06 |
1.13 |
4
| 7.2 |
| Bristol-Myers Squibb |
5
16701 |
14977 |
44.4 |
3.14 |
1.52 |
| 24.1 |
| Burlington Coat |
1
1777 |
775
|
|
|
| 12.3 |
| 1.18 |
0.02 |
12.9 |
| Central Maine Power |
3 954 2299 2.4
|
|
| 0.16 |
|
| 0.9 |
7
| 9.6 |
| Chevron |
7
41950 |
35473 |
18.6 |
4.95 |
2.28 |
1
| 5.2 |
| CIGNA |
6
14935 |
108199 |
| 13.7 |
| 4.8 |
8
1.1
| 11.4 |
| Cinergy |
3
4353 |
8858 |
| 13.3 |
| 1.59 |
1.8
| 22.4 |
| Dayton Hudson |
1
27757 |
14191 |
18
| 1.7 |
| 0.33 |
| 16.2 |
| Dillard’s |
1
6817 |
5592 |
| 9.2 |
2.31 |
0.16
15.7 |
| Dominion Resources |
3
7678 |
20193 |
|
| 7.9 |
2.15 |
2.58 |
1
|
| 7.7 |
| Dow Chemical |
2
20018 |
24040 |
23.6 |
7.7
3.24 |
1
| 1.6 |
| DPL |
3
1356 |
3585 |
13.9 |
1.2
0.91 |
14.3 |
| E. I. DuPont DeNemours |
2
46653 |
42942 |
2
| 1.3 |
2.08
1.23 |
| 27.9 |
| Eastman Chemical |
2
4678 |
5778 |
|
| 16.3 |
3.63 |
1.76 |
16
| Edison International |
3
9235 |
25101 |
12.3
1.73 |
1
13.6 |
| Engelhard |
2
3631 |
2586 |
6.1 0.33
0.38 |
61.8 |
| Entergy |
3
9562 |
27001 |
4.2 |
1.03 |
1.8
25.4 |
| Equitable |
6
9666 |
151438 |
12.3
2.86 |
0.2
| 13.4 |
| Ethyl |
7 1064 1067
53.6 |
0.71 |
|
| 0.5 |
|
| 12.6 |
| Exxon |
7
137242 |
9
| 6064 |
1
| 9.4 |
3.37 |
1.63 |
| 17.1 |
| FPL Group |
3
6369 |
12449 |
12.2
3.57 |
1.92 |
| 14.4 |
| The GAP |
1
6508 |
3338 |
33.7 |
1.3 0.2 22
| Georgia Gulf |
2 966 613 228
| 2.39 |
| 0.32 |
11.8 |
| GIANT Food |
4
4231 |
1522 |
7.9 1.18
0.78 |
2
| 6.9 |
| A & P |
4 10262
2995 |
6.9
| 1.66 |
|
| 0.35 |
1
| 7.8 |
| Great Lakes Chemicals |
2
1311 |
2270 |
5.5 |
| 1.19 |
0.62 |
40.5 |
| Green Mountain Power Company |
3 179 326 8.3
| 1.57 |
1.61 |
14
| Hannaford Bros. |
4 3226 1227
9.9 |
1.4
0.54 |
| 26.6 |
| Hercules |
2
1866 |
2411 |
47
3.18 |
1
|
|
| 14.5 |
| Houston Industries |
3
6873 |
18415 |
8 1.66 1.5 13.7
| Jefferson-Pilot |
6 2578
23131 |
14.5
3.47 |
1.04 13.3
| Johnson & Johnson |
5
22629 |
21453 |
| 26.7 |
2.41 |
0.85 |
24.1
| Liberty |
6 660
3185 |
11.1 |
3.34 |
0.77 |
12.7 |
| The Limited |
1
9189 |
4301 |
10.6
| 0.79 |
| 0.48 |
26.7
| Lincoln National |
6
4899 |
77175 |
0.4
0.21 |
1.96 |
300.2 |
| Lubrizol |
2
1674 |
1462 |
19
2.66 |
1.01 14.5
| Lyondell Petrochemical |
7
3010 |
1559 |
46.2 |
3.58 |
0.9
6.4 |
| Mallinkrodt |
5
1868 |
2988 |
14.8 |
| 2.47 |
0.66 |
16
| May Department Stores |
1
12685 |
9930 |
|
| 20.5 |
3.11 |
1.2 16.2
| McKesson |
5
20857 |
5608 |
11 1.59 0.5 26
| Mercantile Stores |
1
3144 |
2178 7.9
3.53 |
1.19 16.3
| Merck |
5
23637 |
25812 |
36.6 |
3.74 |
1.69 |
26.6
| Millennium Chemicals |
2
3048 |
4326 |
12.6 2.47 0.6 8.3
| Mobil |
7
65906 |
43559 |
16.8 |
4.01 |
2.12 |
| 17.2 |
| Monsanto |
2
7514 |
10774 |
7.2 0.48 0.5
90.7 |
| Morton |
2
2388 |
2805 12.3
| 1.48 |
0.36
25.2 |
| Murphy Oil |
7
2138 |
2238 |
12.3
2.94 |
1.35 |
18
| Mylan Laboratories |
5 555 848
13.5 |
0.82 |
0.16 22.4
| NALCO Chemical |
2
1434 |
1441 25 2.1 1
18.3 |
| Nevada Power |
3 799
2339 |
10.1 |
1.65 |
1.6
| 14.2 |
| NIPSCO |
3 2587
4937 |
14.1 |
1.53 |
0.9 14.4
| Olin |
2
2410 |
1946
| 17.4 |
3 1.2 14.5
| Orion Capital |
6
1591 |
3884 |
16
4.15 |
0.6
9.8 |
| Owens & Minor |
5
3117 |
713 9.4 0.6
0.18 |
21.7 |
| Pacific Corporation |
3
6278 |
13880 |
5.2
| 0.68 |
| 1.08 |
34.2 |
| J. C. Penney |
1
30546 |
23493 |
7.7 2.1
| 2.13 |
26.9 |
| Pennzoil |
7
2654 |
4406 |
1
| 5.8 |
3.76 |
1 17.1
| Pfizer |
5
12504 |
15336 |
27.9 1.7 0.68
35.4 |
| Pharmacia & Upjohn |
5
6710 |
10380 |
5.8
0.61 |
1.08
56.2 |
| Phillips Petroleum |
7
15424 |
13860 |
19.9 |
3.61 |
1.34 |
12.4 |
| Poe & Brown |
6 129 194
25.1 |
1.48 0.35 16.3
| PPG |
2
7379 |
6868 |
28.5 |
3.94 |
1.33 |
| 14.7 |
| PP&L Resources |
3
3049 |
9485 |
11.4 1.8
1.67 |
12
| Progressive |
6
4190 |
7560 |
18.7 |
5.31 |
| 0.24 |
17
| Rohm & Haas |
2
3999 |
3900 |
| 19.8 |
2.13
0.63 |
13.4
| Ruddick |
4 2300 885
12.5 |
1.02 |
0.32 17
| Schering-Plough |
5
6778 |
6507 |
51.2 |
1.95 |
0.74 |
24.6 |
| Sears, Roebuck |
1
41296 |
38700 |
20.3 |
2.99 |
0.92 |
17.4
| Stryker |
5 980
985 |
20.5
1.28 |
0.11 |
| 27.2 |
| Sun |
7
10531 |
4667 |
18 2.7 1 13
| Sunamerica |
6
2114 |
35637 |
14.7 1.8 0.3
19.5 |
| Texaco |
7
46667 |
29600 |
20.9 |
4.87 |
| 1.75 |
11.5
| The TJX Companies |
1
7389 |
2610 |
26.3 |
1.75
0.09 |
8.2 |
| Torchmark |
6
2283 |
10967 |
1
| 7.5 |
2.39
0.59 |
14.2
| Tosco |
7
13282 |
5975 |
10.9 |
1.37 |
0.24 23
| Travelers |
6
37609 |
386555 |
14.9 |
2.54 |
0.4 17
| Ultramar Diamond Shamrock |
7
10882 |
5595 |
9.5
1.94 |
1.1
16.1 |
| Union Carbide |
2
6502 |
6964 |
28.8 |
4.53 |
0.79
10.7 |
| United States Surgical Corporation |
5 1172 1726 7.5
1.21 |
0.16 29
| UNOCAL |
7 6064
7530 |
28.9 |
2.65 |
0.8
15.5 |
| UNUM |
6
4077 |
13200 |
15.2 |
2.59 |
0.56 |
17
| USX-Marathon |
7
15754 |
10565 |
12.6
1.58 |
0.76 |
19.8
| Valero Energy |
7
5756 |
2493 |
9.6
2.03 |
0.42 |
17.2
| Warner-Lambert |
5
8180 |
8031 |
30.7 |
1.04
0.51 |
35.7 |
| WEIS Markets |
4 1819 972 9.2
1.87 |
0.94 |
16.9 |
| Wellman |
2
1083 |
1319 |
4.8
0.97 |
0.35 20.5
| Winn-Dixie Stores |
4 13219
2921 |
15.3 |
1.36 |
0.98 |
27.2
| WITCO |
2 2187
2298 |
14
1.55 |
1.12 |
24.9 |
| Zenith Nation Insurance |
6 601 1252 7.8 1.57 1 17
|
Title
ABC/
1
23 Version X
|
|
1
|
|
Week 6 Options
QNT/561 Version 9
|
1
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
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.
Copyright © XXXX by University of Phoenix. All rights reserved.
Copyright © 2017 by University of Phoenix. All rights reserved.
