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Essentials of Modern Business Statistics with Microsoft Office Excel 7th Edition Anderson Solutions Manual

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Essentials of Modern Business Statistics with Microsoft Office Excel 7th Edition Anderson Solutions Manual.

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Discover an accessible introduction to business statistics as ESSENTIALS OF MODERN BUSINESS STATISTICS, 7E balances a conceptual understanding of statistics with real-world applications of statistical methodology. The book integrates Microsoft Excel 2016, providing step-by-step instructions and screen captures to help you master the latest Excel tools. Extremely reader-friendly, this edition includes numerous tools to maximize your course success, including Self-Test Exercises, margin annotations, insightful Notes and Comments, and real-world Methods and Applications exercises. Eleven new Case Problems, as well as new Statistics in Practice applications and real data examples and exercises, give you opportunities to put what you learn into practice. Additional learning resources, including MindTap and CengageNOW for online homework assistance and a complete support website, provide everything to acquire Excel 2016 skills and an understanding of business statistics.

 

Table of Content:

  1. Chapter 1: Data and Statistics
  2. Statistics In Practice: Bloomberg Businessweek
  3. 1.1 Applications in Business and Economics
  4. Accounting
  5. Finance
  6. Marketing
  7. Production
  8. Economics
  9. Information Systems
  10. 1.2 Data
  11. Elements, Variables, and Observations
  12. Scales of Measurement
  13. Categorical and Quantitative Data
  14. Cross-Sectional and Time Series Data
  15. 1.3 Data Sources
  16. Existing Sources
  17. Observational Study
  18. Experiment
  19. Time and Cost Issues
  20. Data Acquisition Errors
  21. 1.4 Descriptive Statistics
  22. 1.5 Statistical Inference
  23. 1.6 Statistical Analysis Using Microsoft Excel
  24. Data Sets and Excel Worksheets
  25. Using Excel for Statistical Analysis
  26. 1.7 Analytics
  27. 1.8 Big Data and Data Mining
  28. 1.9 Ethical Guidelines for Statistical Practice
  29. Summary
  30. Glossary
  31. Supplementary Exercises
  32. Chapter 2: Descriptive Statistics: Tabular and Graphical Displays
  33. Statistics In Practice: Colgate-Palmolive Company
  34. 2.1 Summarizing Data for a Categorical Variable
  35. Frequency Distribution
  36. Relative Frequency and Percent Frequency Distributions
  37. Using Excel to Construct a Frequency Distribution, a Relative Frequency Distribution, and a Percent
  38. Bar Charts and Pie Charts
  39. Using Excel to Construct a Bar Chart and a Pie Chart
  40. 2.2 Summarizing Data for a Quantitative Variable
  41. Frequency Distribution
  42. Relative Frequency and Percent Frequency Distributions
  43. Using Excel to Construct a Frequency Distribution
  44. Dot Plot
  45. Histogram
  46. Using Excel’s Recommended Charts Tool to Construct a Histogram
  47. Cumulative Distributions
  48. Stem-and-Leaf Display
  49. 2.3 Summarizing Data for Two Variables Using Tables
  50. Crosstabulation
  51. Using Excel’s PivotTable Tool to Construct a Crosstabulation
  52. Simpson’s Paradox
  53. 2.4 Summarizing Data for Two Variables Using Graphical Displays
  54. Scatter Diagram and Trendline
  55. Using Excel to Construct a Scatter Diagram and a Trendline
  56. Side-by-Side and Stacked Bar Charts
  57. Using Excel’s Recommended Charts Tool to Construct Side-by-Side and Stacked Bar Charts
  58. 2.5 Data Visualization: Best Practices in Creating Effective Graphical Displays
  59. Creating Effective Graphical Displays
  60. Choosing the Type of Graphical Display
  61. Data Dashboards
  62. Data Visualization in Practice: Cincinnati Zoo and Botanical Garden
  63. Summary
  64. Glossary
  65. Key Formulas
  66. Supplementary Exercises
  67. Case Problem 1: Pelican Stores
  68. Case Problem 2: Motion Picture Industry
  69. Case Problem 3: Queen City
  70. Case Problem 4: Cut-Rate Machining, Inc.
  71. Chapter 3: Descriptive Statistics: Numerical Measures
  72. Statistics In Practice: Small Fry Design
  73. 3.1 Measures of Location
  74. Mean
  75. Median
  76. Mode
  77. Using Excel to Compute the Mean, Median, and Mode
  78. Weighted Mean
  79. Geometric Mean
  80. Using Excel to Compute the Geometric Mean
  81. Percentiles
  82. Quartiles
  83. Using Excel to Compute Percentiles and Quartiles
  84. 3.2 Measures of Variability
  85. Range
  86. Interquartile Range
  87. Variance
  88. Standard Deviation
  89. Using Excel to Compute the Sample Variance and Sample Standard Deviation
  90. Coefficient of Variation
  91. Using Excel’s Descriptive Statistics Tool
  92. 3.3 Measures of Distribution Shape, Relative Location, and Detecting Outliers
  93. Distribution Shape
  94. z-Scores
  95. Chebyshev’s Theorem
  96. Empirical Rule
  97. Detecting Outliers
  98. 3.4 Five-Number Summaries and Box Plots
  99. Five-Number Summary
  100. Box Plot
  101. Using Excel to Construct a Box Plot
  102. Comparative Analysis Using Box Plots
  103. Using Excel to Construct a Comparative Analysis Using Box Plots
  104. 3.5 Measures of Association Between Two Variables
  105. Covariance
  106. Interpretation of the Covariance
  107. Correlation Coefficient
  108. Interpretation of the Correlation Coefficient
  109. Using Excel to Compute the Sample Covariance and Sample Correlation Coefficient
  110. 3.6 Data Dashboards: Adding Numerical Measures to Improve Effectiveness
  111. Summary
  112. Glossary
  113. Key Formulas
  114. Supplementary Exercises
  115. Case Problem 1: Pelican Stores
  116. Case Problem 2: Motion Picture Industry
  117. Case Problem 3: Business Schools of Asia-Pacific
  118. Case Problem 4: Heavenly Chocolates Website Transactions
  119. Case Problem 5: African Elephant Populations
  120. Chapter 4: Introduction to Probability
  121. Statistics In Practice: National Aeronautics And Space Administration
  122. 4.1 Experiments, Counting Rules, and Assigning Probabilities
  123. Counting Rules, Combinations, and Permutations
  124. Assigning Probabilities
  125. Probabilities for the KP&L Project
  126. 4.2 Events and Their Probabilities
  127. 4.3 Some Basic Relationships of Probability
  128. Complement of an Event
  129. Addition Law
  130. 4.4 Conditional Probability
  131. Independent Events
  132. Multiplication Law
  133. 4.5 Bayes’ Theorem
  134. Tabular Approach
  135. Summary
  136. Glossary
  137. Key Formulas
  138. Supplementary Exercises
  139. Case Problem 1: Hamilton County Judges
  140. Case Problem 2: Rob’s Market
  141. Chapter 5: Discrete Probability Distributions
  142. Statistics In Practice: Citibank
  143. 5.1 Random Variables
  144. Discrete Random Variables
  145. Continuous Random Variables
  146. 5.2 Developing Discrete Probability Distributions
  147. 5.3 Expected Value and Variance
  148. Expected Value
  149. Variance
  150. Using Excel to Compute the Expected Value, Variance, and Standard Deviation
  151. 5.4 Bivariate Distributions, Covariance, and Financial Portfolios
  152. A Bivariate Empirical Discrete Probability Distribution
  153. Financial Applications
  154. Summary
  155. 5.5 Binomial Probability Distribution
  156. A Binomial Experiment
  157. Martin Clothing Store Problem
  158. Using Excel to Compute Binomial Probabilities
  159. Expected Value and Variance for the Binomial Distribution
  160. 5.6 Poisson Probability Distribution
  161. An Example Involving Time Intervals
  162. An Example Involving Length or Distance Intervals
  163. Using Excel to Compute Poisson Probabilities
  164. 5.7 Hypergeometric Probability Distribution
  165. Using Excel to Compute Hypergeometric Probabilities
  166. Summary
  167. Glossary
  168. Key Formulas
  169. Supplementary Exercises
  170. Case Problem 1: Go Bananas!
  171. Case Problem 2: McNeil’s Auto Mall
  172. Case Problem 3: Grievance Committee at Tuglar Corporation
  173. Case Problem 4: Sagittarius Casino
  174. Chapter 6: Continuous Probability Distributions
  175. Statistics In Practice: Procter & Gamble
  176. 6.1 Uniform Probability Distribution
  177. Area as a Measure of Probability
  178. 6.2 Normal Probability Distribution
  179. Normal Curve
  180. Standard Normal Probability Distribution
  181. Computing Probabilities for Any Normal Probability Distribution
  182. Grear Tire Company Problem
  183. Using Excel to Compute Normal Probabilities
  184. 6.3 Exponential Probability Distribution
  185. Computing Probabilities for the Exponential Distribution
  186. Relationship Between the Poisson and Exponential Distributions
  187. Using Excel to Compute Exponential Probabilities
  188. Summary
  189. Glossary
  190. Key Formulas
  191. Supplementary Exercises
  192. Case Problem 1: Specialty Toys
  193. Case Problem 2: Gebhardt Electronics
  194. Chapter 7: Sampling and Sampling Distributions
  195. Statistics In Practice: Meadwestvaco Corporation
  196. 7.1 The Electronics Associates Sampling Problem
  197. 7.2 Selecting a Sample
  198. Sampling from a Finite Population
  199. Sampling from an Infinite Population
  200. 7.3 Point Estimation
  201. Practical Advice
  202. 7.4 Introduction to Sampling Distributions
  203. 7.5 Sampling Distribution of x_(Bar)
  204. Expected Value of x_(Bar)
  205. Standard Deviation of x_(Bar)
  206. Form of the Sampling Distribution of x_(Bar)
  207. Sampling Distribution of x_(Bar) for the EAI Problem
  208. Practical Value of the Sampling Distribution of x_(Bar)
  209. Relationship Between the Sample Size and the Sampling Distribution of x_(Bar)
  210. 7.6 Sampling Distribution of p_(Bar)
  211. Expected Value of p_(Bar)
  212. Standard Deviation of p_(Bar)
  213. Form of the Sampling Distribution of p_(Bar)
  214. Practical Value of the Sampling Distribution of p_(Bar)
  215. 7.7 Other Sampling Methods
  216. Stratified Random Sampling
  217. Cluster Sampling
  218. Systematic Sampling
  219. Convenience Sampling
  220. Judgment Sampling
  221. 7.8 Practical Advice: Big Data and Errors in Sampling
  222. Sampling Error
  223. Nonsampling Error
  224. Implications of Big Data
  225. Summary
  226. Glossary
  227. Key Formulas
  228. Supplementary Exercises
  229. Case Problem 1: Marion Dairies
  230. Chapter 8: Interval Estimation
  231. Statistics In Practice: Food Lion
  232. 8.1 Population Mean: sigma Known
  233. Margin of Error and the Interval Estimate
  234. Using Excel
  235. Practical Advice
  236. 8.2 Population Mean: sigma Unknown
  237. Margin of Error and the Interval Estimate
  238. Using Excel
  239. Practical Advice
  240. Using a Small Sample
  241. Summary of Interval Estimation Procedures
  242. 8.3 Determining the Sample Size
  243. 8.4 Population Proportion
  244. Using Excel
  245. Determining the Sample Size
  246. 8.5 Practical Advice: Big Data and Interval Estimation
  247. Big Data and the Precision of Confidence Intervals
  248. Implications of Big Data
  249. Summary
  250. Glossary
  251. Key Formulas
  252. Supplementary Exercises
  253. Case Problem 1: Young Professional Magazine
  254. Case Problem 2: Gulf Real Estate Properties
  255. Case Problem 3: Metropolitan Research, Inc.
  256. Chapter 9: Hypothesis Tests
  257. Statistics In Practice: John Morrell & Company
  258. 9.1 Developing Null and Alternative Hypotheses
  259. The Alternative Hypothesis as a Research Hypothesis
  260. The Null Hypothesis as an Assumption to Be Challenged
  261. Summary of Forms for Null and Alternative Hypotheses
  262. 9.2 Type I and Type II Errors
  263. 9.3 Population Mean: sigma Known
  264. One-Tailed Test
  265. Two-Tailed Test
  266. Using Excel
  267. Summary and Practical Advice
  268. Relationship Between Interval Estimation and Hypothesis Testing
  269. 9.4 Population Mean: sigma Unknown
  270. One-Tailed Test
  271. Two-Tailed Test
  272. Using Excel
  273. Summary and Practical Advice
  274. 9.5 Population Proportion
  275. Using Excel
  276. Summary
  277. 9.6 Practical Advice: Big Data and Hypothesis Testing
  278. Big Data and p-Values
  279. Implications of Big Data
  280. Summary
  281. Glossary
  282. Key Formulas
  283. Supplementary Exercises
  284. Case Problem 1: Quality Associates, Inc.
  285. Case Problem 2: Ethical Behavior of Business Students at Bayview University
  286. Chapter 10: Inference About Means and Proportions with Two Populations
  287. Statistics In Practice: U.S. Food And Drug Administration
  288. 10.1 Inferences About the Difference Between Two Population Means: sigma(subscript_1) and sigma(subs
  289. Interval Estimation of mu(subscript_1) – mu(subscript_2)
  290. Using Excel to Construct a Confidence Interval
  291. Hypothesis Tests About mu(subscript_1) – mu(subscript_2)
  292. Using Excel to Conduct a Hypothesis Test
  293. Practical Advice
  294. 10.2 Inferences About the Difference Between Two Population Means: sigma(subscript_1) and sigma(subs
  295. Interval Estimation of mu(subscript_1) – mu(subscript_2)
  296. Using Excel to Construct a Confidence Interval
  297. Hypothesis Tests About mu(subscript_1) – mu(subscript_2)
  298. Using Excel to Conduct a Hypothesis Test
  299. Practical Advice
  300. 10.3 Inferences About the Difference Between Two Population Means: Matched Samples
  301. Using Excel to Conduct a Hypothesis Test
  302. 10.4 Inferences About the Difference Between Two Population Proportions
  303. Interval Estimation of p(subscript_1) – (subscript_2)
  304. Using Excel to Construct a Confidence Interval
  305. Hypothesis Tests About p(subscript_1) – p(subscript_2)
  306. Using Excel to Conduct a Hypothesis Test
  307. Summary
  308. Glossary
  309. Key Formulas
  310. Supplementary Exercises
  311. Case Problem: Par, Inc.
  312. Chapter 11: Inferences About Population Variances
  313. Statistics In Practice: U.S. Government Accountability Office
  314. 11.1 Inferences About a Population Variance
  315. Interval Estimation
  316. Using Excel to Construct a Confidence Interval
  317. Hypothesis Testing
  318. Using Excel to Conduct a Hypothesis Test
  319. 11.2 Inferences About Two Population Variances
  320. Using Excel to Conduct a Hypothesis Test
  321. Summary
  322. Key Formulas
  323. Supplementary Exercises
  324. Case Problem 1: Air Force Training Program
  325. Case Problem 2: Meticulous Drill & Reamer
  326. Chapter 12: Tests of Goodness of Fit, Independence, and Multiple Proportions
  327. Statistics In Practice: United Way
  328. 12.1 Goodness of Fit Test
  329. Multinomial Probability Distribution
  330. Using Excel to Conduct a Goodness of Fit Test
  331. 12.2 Test of Independence
  332. Using Excel to Conduct a Test of Independence
  333. 12.3 Testing for Equality of Three or More Population Proportions
  334. A Multiple Comparison Procedure
  335. Using Excel to Conduct a Test of Multiple Proportions
  336. Summary
  337. Glossary
  338. Key Formulas
  339. Supplementary Exercises
  340. Case Problem 1: A Bipartisan Agenda for Change
  341. Case Problem 2: Fuentes Salty Snacks, Inc.
  342. Case Problem 3: Fresno Board Games
  343. Chapter 13: Experimental Design and Analysis of Variance
  344. Statistics In Practice: Burke Marketing Services, Inc.
  345. 13.1 An Introduction to Experimental Design and Analysis of Variance
  346. Data Collection
  347. Assumptions for Analysis of Variance
  348. Analysis of Variance: A Conceptual Overview
  349. 13.2 Analysis of Variance and the Completely Randomized Design
  350. Between-Treatments Estimate of Population Variance
  351. Within-Treatments Estimate of Population Variance
  352. Comparing the Variance Estimates: The F Test
  353. ANOVA Table
  354. Using Excel
  355. Testing for the Equality of k Population Means: An Observational Study
  356. 13.3 Multiple Comparison Procedures
  357. Fisher’s LSD
  358. Type I Error Rates
  359. 13.4 Randomized Block Design
  360. Air Traffic Controller Stress Test
  361. ANOVA Procedure
  362. Computations and Conclusions
  363. Using Excel
  364. 13.5 Factorial Experiment
  365. ANOVA Procedure
  366. Computations and Conclusions
  367. Using Excel
  368. Summary
  369. Glossary
  370. Key Formulas
  371. Supplementary Exercises
  372. Case Problem 1: Wentworth Medical Center
  373. Case Problem 2: Compensation for Sales Professionals
  374. Case Problem 3: TourisTopia Travel
  375. Chapter 14: Simple Linear Regression
  376. Statistics In Practice: Alliance Data Systems
  377. 14.1 Simple Linear Regression Model
  378. Regression Model and Regression Equation
  379. Estimated Regression Equation
  380. 14.2 Least Squares Method
  381. Using Excel to Construct a Scatter Diagram, Display the Estimated Regression Line, and Display the E
  382. 14.3 Coefficient of Determination
  383. Using Excel to Compute the Coefficient of Determination
  384. Correlation Coefficient
  385. 14.4 Model Assumptions
  386. 14.5 Testing for Significance
  387. Estimate of sigma(superscript2)
  388. t Test
  389. Confidence Interval for Bita(subscript1)
  390. F Test
  391. Some Cautions About the Interpretation of Significance Tests
  392. 14.6 Using the Estimated Regression Equation for Estimation and Prediction
  393. Interval Estimation
  394. Confidence Interval for the Mean Value of y
  395. Prediction Interval for an Individual Value of y
  396. 14.7 Excel’s Regression Tool
  397. Using Excel’s Regression Tool for the Armand’s Pizza Parlors Example
  398. Interpretation of Estimated Regression Equation Output
  399. Interpretation of ANOVA Output
  400. Interpretation of Regression Statistics Output
  401. 14.8 Residual Analysis: Validating Model Assumptions
  402. Residual Plot Against x
  403. Residual Plot Against y(Bar)
  404. Standardized Residuals
  405. Using Excel to Construct a Residual Plot
  406. Normal Probability Plot
  407. 14.9 Outliers and Influential Observations
  408. Detecting Outliers
  409. Detecting Influential Observations
  410. 14.10 Practical Advice: Big Data and Hypothesis Testing in Simple Linear Regression
  411. Summary
  412. Glossary
  413. Key Formulas
  414. Supplementary Exercises
  415. Case Problem 1: Measuring Stock Market Risk
  416. Case Problem 2: U.S. Department of Transportation
  417. Case Problem 3: Selecting a Point-and-Shoot Digital Camera
  418. Case Problem 4: Finding the Best Car Value
  419. Case Problem 5: Buckeye Creek Amusement Park
  420. Appendix 14.1: Calculus-Based Derivation of Least Squares Formulas
  421. Appendix 14.2: A Test for Significance Using Correlation
  422. Chapter 15: Multiple Regression
  423. Statistics In Practice: International Paper
  424. 15.1 Multiple Regression Model
  425. Regression Model and Regression Equation
  426. Estimated Multiple Regression Equation
  427. 15.2 Least Squares Method
  428. An Example: Butler Trucking Company
  429. Using Excel’s Regression Tool to Develop the Estimated Multiple Regression Equation
  430. Note on Interpretation of Coefficients
  431. 15.3 Multiple Coefficient of Determination
  432. 15.4 Model Assumptions
  433. 15.5 Testing for Significance
  434. F Test
  435. t Test
  436. Multicollinearity
  437. 15.6 Using the Estimated Regression Equation for Estimation and Prediction
  438. 15.7 Categorical Independent Variables
  439. An Example: Johnson Filtration, Inc.
  440. Interpreting the Parameters
  441. More Complex Categorical Variables
  442. 15.8 Residual Analysis
  443. Residual Plot Against y⁄
  444. Standardized Residual Plot Against y⁄
  445. Practical Advice: Big Data And Hypothesis Testing In Multiple Regression
  446. Summary
  447. Glossary
  448. Key Formulas
  449. Supplementary Exercises
  450. Case Problem 1: Consumer Research, Inc.
  451. Case Problem 2: Predicting Winnings for NASCAR Drivers
  452. Case Problem 3: Finding the Best Car Value
  453. Appendix A: References and Bibliography
  454. Appendix B: Tables
  455. Appendix C: Summation Notation
  456. Appendix D: Self-Test Solutions and Answers to Even-Numbered Exercises (online)
  457. Appendix E: Microsoft Excel 2016 and Tools for Statistical Analysis
  458. Index

 

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