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 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.
Plagiarism Free
>Option – Manufacturer
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Option 2 –
| 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
| 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
| 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 |
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Title ABC/ 1 23 Version X |
1 |
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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 |
|
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 2 3 Version X |
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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.