### Question Description

### Question Description

About Your Signature Assignment

This signature assignment is designed to align with specific program student learning outcome(s) in your program. Program Student Learning Outcomes are broad statements that describe what students should know and be able to do upon completion of their degree. The signature assignments might be graded with an automated rubric that allows the University to collect data that can be aggregated across a location or college/school and used for program improvements.

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.

Assignment Steps

Resources: Microsoft Excel®, Signature Assignment Databases, Signature Assignment Options, Part 3: Inferential Statistics

Scenario: Upon successful completion of the MBA program, say 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, statistical report including the following:

- Explain the context of the case
- Provide a research foundation for the topic
- Present graphs
- Explain outliers
- Prepare calculations
- Conduct hypotheses tests
- Discuss inferences you have made from the results

This assignment is broken down into four parts:

- Part 1 - Preliminary Analysis
- Part 2 - Examination of Descriptive Statistics
- Part 3 - Examination of Inferential Statistics
- Part 4 - Conclusion/Recommendations

Part 1 - Preliminary Analysis (3-4 paragraphs)

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 (3-4 paragraphs)

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 (2-3 paragraphs)

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 (1-2 paragraphs)

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.

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

SIC Code | No. Emp. | No. Prod. Wkrs. | Value Added by Mfg. | Cost of Materials | End Yr. Inven. | Indus. Grp. | |

201 | 433 | 370 | 23518 | 78713 | 3630 | 1 | |

202 | 131 | 83 | 15724 | 42774 | 3157 | 1 | |

203 | 204 | 169 | 24506 | 27222 | 8732 | 1 | |

204 | 100 | 70 | 21667 | 37040 | 3407 | 1 | |

205 | 220 | 137 | 20712 | 12030 | 1155 | 1 | |

206 | 89 | 69 | 12640 | 13674 | 3613 | 1 | |

207 | 26 | 18 | 4258 | 19130 | 1946 | 1 | |

208 | 143 | 72 | 35210 | 33521 | 7199 | 1 | |

209 | 171 | 126 | 20548 | 19612 | 3135 | 1 | |

211 | 21 | 15 | 23442 | 5557 | 5506 | 2 | |

212 | 3 | 2 | 287 | 163 | 42 | 2 | |

213 | 2 | 2 | 1508 | 314 | 155 | 2 | |

214 | 6 | 4 | 624 | 2622 | 554 | 2 | |

221 | 52 | 47 | 2471 | 4219 | 929 | 3 | |

222 | 74 | 63 | 4307 | 5357 | 1427 | 3 | |

223 | 13 | 12 | 673 | 1061 | 325 | 3 | |

224 | 17 | 13 | 817 | 707 | 267 | 3 | |

225 | 169 | 147 | 8986 | 10421 | 2083 | 3 | |

226 | 51 | 41 | 3145 | 4140 | 697 | 3 | |

227 | 55 | 44 | 4076 | 7125 | 1446 | 3 | |

228 | 84 | 76 | 3806 | 8994 | 1014 | 3 | |

229 | 61 | 47 | 4276 | 5504 | 1291 | 3 | |

231 | 27 | 22 | 1239 | 716 | 356 | 4 | |

232 | 200 | 178 | 9423 | 8926 | 2314 | 4 | |

233 | 294 | 250 | 11045 | 11121 | 2727 | 4 | |

234 | 38 | 32 | 1916 | 2283 | 682 | 4 | |

235 | 17 | 14 | 599 | 364 | 197 | 4 | |

236 | 34 | 28 | 2063 | 1813 | 450 | 4 | |

237 | 1 | 1 | 34 | 71 | 17 | 4 | |

238 | 31 | 25 | 1445 | 1321 | 526 | 4 | |

239 | 224 | 179 | 10603 | 12376 | 2747 | 4 | |

241 | 83 | 68 | 5775 | 9661 | 578 | 5 | |

242 | 172 | 147 | 10404 | 19285 | 3979 | 5 | |

243 | 257 | 209 | 13274 | 18632 | 3329 | 5 | |

244 | 51 | 43 | 1909 | 2170 | 355 | 5 | |

245 | 82 | 68 | 4606 | 7290 | 580 | 5 | |

249 | 94 | 78 | 5518 | 8135 | 1604 | 5 | |

251 | 273 | 233 | 12464 | 12980 | 3535 | 6 | |

252 | 70 | 53 | 5447 | 4011 | 829 | 6 | |

253 | 37 | 29 | 2290 | 5101 | 447 | 6 | |

254 | 81 | 61 | 4182 | 3755 | 956 | 6 | |

259 | 54 | 39 | 2818 | 2694 | 718 | 6 | |

261 | 15 | 11 | 2201 | 3279 | 725 | 7 | |

262 | 116 | 90 | 18848 | 20596 | 4257 | 7 | |

263 | 55 | 42 | 9655 | 10604 | 1502 | 7 | |

265 | 212 | 163 | 15668 | 24634 | 3976 | 7 | |

267 | 232 | 182 | 25918 | 28963 | 5427 | 7 | |

271 | 403 | 136 | 30692 | 8483 | 894 | 8 | |

272 | 121 | 16 | 17982 | 6940 | 1216 | 8 | |

273 | 136 | 57 | 17857 | 8863 | 3736 | 8 | |

274 | 69 | 25 | 9699 | 2823 | 874 | 8 | |

275 | 604 | 437 | 38407 | 29572 | 4300 | 8 | |

276 | 41 | 28 | 3878 | 3811 | 688 | 8 | |

277 | 21 | 12 | 3989 | 1047 | 577 | 8 | |

278 | 65 | 50 | 4388 | 2055 | 504 | 8 | |

279 | 55 | 39 | 4055 | 1098 | 236 | 8 | |

281 | 80 | 45 | 16567 | 11298 | 2644 | 9 | |

282 | 115 | 79 | 25025 | 34596 | 6192 | 9 | |

283 | 213 | 106 | 59813 | 27187 | 11533 | 9 | |

284 | 126 | 75 | 31801 | 19932 | 4535 | 9 | |

285 | 51 | 28 | 8497 | 9849 | 2178 | 9 | |

286 | 126 | 75 | 28886 | 46935 | 8577 | 9 | |

287 | 37 | 24 | 12277 | 11130 | 2354 | 9 | |

289 | 76 | 45 | 11547 | 13085 | 2749 | 9 | |

291 | 67 | 43 | 26006 | 132880 | 10718 | 10 | |

295 | 25 | 18 | 3464 | 6182 | 658 | 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 | 1057 | 11 | |

306 | 122 | 95 | 7275 | 7195 | 1411 | 11 | |

308 | 763 | 598 | 55621 | 57264 | 11874 | 11 | |

311 | 15 | 12 | 1313 | 1865 | 404 | 12 | |

313 | 3 | 2 | 162 | 163 | 35 | 12 | |

314 | 37 | 31 | 1907 | 1682 | 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 | 1717 | 943 | 282 | 13 | |

322 | 60 | 51 | 6532 | 3527 | 1505 | 13 | |

323 | 64 | 50 | 4850 | 4254 | 883 | 13 | |

324 | 17 | 13 | 3509 | 2282 | 828 | 13 | |

325 | 31 | 25 | 2176 | 1387 | 700 | 13 | |

326 | 45 | 36 | 2696 | 1183 | 600 | 13 | |

327 | 205 | 152 | 15739 | 17010 | 1966 | 13 | |

328 | 17 | 13 | 999 | 565 | 263 | 13 | |

329 | 72 | 53 | 7838 | 5432 | 1652 | 13 | |

331 | 221 | 174 | 29180 | 45696 | 12198 | 14 | |

332 | 128 | 106 | 9061 | 6913 | 1543 | 14 | |

333 | 35 | 26 | 4200 | 11184 | 1834 | 14 | |

334 | 15 | 11 | 1410 | 5735 | 694 | 14 | |

335 | 162 | 123 | 16670 | 31892 | 6377 | 14 | |

336 | 94 | 79 | 5856 | 4696 | 938 | 14 | |

339 | 32 | 23 | 3164 | 2790 | 800 | 14 | |

341 | 33 | 27 | 3999 | 9364 | 1453 | 15 | |

342 | 140 | 107 | 11750 | 8720 | 3124 | 15 | |

343 | 45 | 32 | 4412 | 3527 | 1121 | 15 | |

344 | 432 | 315 | 27974 | 31527 | 7204 | 15 | |

345 | 104 | 81 | 6936 | 4909 | 1768 | 15 | |

346 | 259 | 211 | 19880 | 21531 | 3997 | 15 | |

347 | 129 | 99 | 7793 | 6232 | 1181 | 15 | |

348 | 40 | 24 | 3528 | 1689 | 1077 | 15 | |

349 | 300 | 219 | 21718 | 19273 | 6460 | 15 | |

351 | 79 | 55 | 10513 | 12954 | 3679 | 16 | |

352 | 94 | 70 | 9545 | 11858 | 3339 | 16 | |

353 | 205 | 133 | 18178 | 23474 | 7344 | 16 | |

354 | 295 | 211 | 22673 | 14343 | 6730 | 16 | |

355 | 192 | 110 | 19221 | 16515 | 6823 | 16 | |

356 | 265 | 172 | 23110 | 18543 | 7898 | 16 | |

357 | 259 | 96 | 41135 | 60857 | 10277 | 16 | |

358 | 201 | 147 | 17521 | 21819 | 4857 | 16 | |

359 | 392 | 293 | 25322 | 13897 | 4964 | 16 | |

361 | 74 | 51 | 6700 | 5523 | 1495 | 17 | |

362 | 171 | 120 | 14278 | 12657 | 3887 | 17 | |

363 | 108 | 87 | 9466 | 12578 | 2299 | 17 | |

364 | 157 | 117 | 13428 | 11065 | 3076 | 17 | |

365 | 49 | 37 | 3459 | 7621 | 1070 | 17 | |

366 | 258 | 120 | 38705 | 29591 | 9467 | 17 | |

367 | 588 | 368 | 84059 | 44486 | 13145 | 17 | |

369 | 151 | 106 | 13920 | 13398 | 3514 | 17 | |

371 | 772 | 634 | 105899 | 223639 | 15852 | 18 | |

372 | 377 | 190 | 45220 | 42367 | 36814 | 18 | |

373 | 141 | 108 | 7903 | 7760 | 2165 | 18 | |

374 | 31 | 23 | 2590 | 4363 | 1233 | 18 | |

375 | 18 | 14 | 1435 | 1674 | 412 | 18 | |

376 | 81 | 29 | 9986 | 8120 | 4770 | 18 | |

379 | 47 | 35 | 3564 | 5476 | 1102 | 18 | |

381 | 186 | 68 | 21071 | 8760 | 6183 | 19 | |

382 | 272 | 141 | 29028 | 18028 | 7681 | 19 | |

384 | 268 | 157 | 31051 | 16787 | 7761 | 19 | |

385 | 27 | 17 | 2390 | 1020 | 426 | 19 | |

386 | 61 | 36 | 14032 | 8114 | 2290 | 19 | |

387 | 6 | 4 | 415 | 382 | 177 | 19 | |

391 | 43 | 30 | 2761 | 3646 | 1451 | 20 | |

393 | 13 | 10 | 685 | 506 | 328 | 20 | |

394 | 103 | 76 | 8327 | 6604 | 2608 | 20 | |

395 | 35 | 26 | 2643 | 1789 | 799 | 20 | |

396 | 24 | 19 | 1406 | 997 | 415 | 20 | |

399 | 179 | 123 | 11199 | 8530 | 2861 | 20 | |

*Part 3: Inferential Statistics *

Option 1: Manufacturing Database

- 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?
- 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.
- 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.
- 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

- 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?
- 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?
- 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.
- 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

- 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.
- 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.
- 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

- 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.
- 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.
- 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.
- 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.

*Part 3: Inferential Statistics *

Option 1: Manufacturing Database

- 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?
- 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.
- 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.
- 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

- 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?
- 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?
- 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.
- 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

- 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.
- 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.
- 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

- 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.
- 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.
- 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.
- 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.

## Final Answer

Attached.

Running head: MANUFACTURING DATABASE

MANUFACTURING DATABASE

Name:

Institution affiliation:

Date:

1

MANUFACTURING DATABASE

2

Part I: Preliminary Analysis

Objective

The key goals of this research are:

•

To determine the average number of production workers.

•

To verify if the mean number of workers per industry group is less than 150 (1000s).

•

To construct a 95% confidence interval for the population mean number of production

workers.

•

To evaluate if a significant difference exists between the mean Cost of Materials in

manufacturing and mean Value Added by the Manufacturer.

•

To evaluate if there is a significant difference in variance between Cost of Materials as

compared to the End-of-Year Inventories.

Population

The dataset consists of 6 variables obtained from 20 industries and 140 sub-industries in

the U.S. The variables include; the Number of Employees, Number of Production Workers,

Value Added by Manufacture, Cost of Materials. End-of-Year Inventories, and Industry Groups.

The two variables, i.e., the Number of Production Workers and Number of Employees are in

units of 1000. Three variables, i.e., Cost of Materials, Value Added by Manufacture, and End-ofYear Inventories are in million-dollar units. The Industry Group variable consists of numbers

from 1-20 to denote the industry group to which the specific sub-industry belongs.

Data & variables

The provided data is qualitative in the sense that it consists of measurable numbers. The

five qualitative variables in the data set are No. Emp., No. Prod. Wkrs., Value Added by Mfg.,

Cost of Materials, and End Yr. Inven. These variables were measured on a ratio scale of

MANUFACTURING DATABASE

3

measurement. Indus. On the other hand, the Indus. Grp. is a qualitative variable that is measured

on a nominal scale of measurement.

Part II: Descriptive Statistics

The following table provides a summary of the mean, mode, median, standard deviation, range,

CV, variance, and five-number summary.

Value

No.

No. Prod.

Added by

Cost of

End Yr.

Emp.

Wkrs.

Mfg.

Materials

Inven.

Indus. Grp.

mean

123.7929 86.95714286

12497.67857 14109.77143 3050.792857 10.557143

median

75

52

7534

7455.5

1498.5

11

mode

17

2

#N/A

163

#N/A

1

minimum

1

1

34

71

17

1

maximum

772

634

105899

223639

36814

20

range

771

633

105865

223568

36797

19

standard

138.0624 102.1639162

15340.69054 24569.91329 4242.622069 5.8312868

variance

19061.22 10437.46578

235336786.3 603680639.1 17999842.02 34.003905

CV

112%

117%

123%

174%

139%

55%

1st quartile

33.75

24.75

2682.75

2676

686.5

5

3rd quartile

171.25

117.75

17888.25

16583

3773.75

16

93

15205.5

13907

3087.25

11

257.25

40696.5

37443.5

8404.625

32.5

-114.75

-20125.5

-18184.5

-3944.375

-11.5

deviation

interquartile 137.5

range

UPPER

377.5

bound

Lower bond -172.5

MANUFACTURING DATABASE

4

From the above table, the mean of the number of Employees, number of Production

Workers, value Added by Manufacturer, Cost of Materials, and End-of-Year Inventories is

123.79, 86.5, 12497.68, 14109.77, and 3050.79 respectively. On the other hand, the median for

the Number of Employees, Number of Production Workers, value Added by Manufacture, Cost

of Materials and End-of-Year Inventories is 75, 52, 7534, 7455.5 and 1498.5 respectively. The

value of standard deviation is high, implying the unreliability of mean and probable existence of

outliers in the data set. Almost all variables have a high value of standard deviation except Indus.

Grp.

Outliers

Outliers are data points that vary significantly from the rest of the data points. The

presence of outliers indicates an uneven distribution of data or the presence of errors. The data

set comprises of several outliers. Both the Number of Employees and Number of Production

Workers have 8 outliers. The Value Added by Manufacturer has 6 outliers, Cost of Material

has10 outliers, and End of Year Inventories consists of 11 outliers.

Charts

Histogram

MANUFACTURING DATABASE

5

From the above histograms, it is evident that the data for Number of Production Workers,

Number of Employees, Cost of Materials, value Added by Manufacture, and End-of-Year

Inventories is skewed to the right. In this case, only a few observations have high values.

The boxplot

The figure below presents the box plot for Number of Employees, Number of Production

Workers, value Added by Manufacture, Cost of Materials, and End-of-Year Invento...

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