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Essentials of Business Analytics 2nd Edition Camm Solutions Manual

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Essentials of Business Analytics 2nd Edition Camm Solutions Manual

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ESSENTIALS OF BUSINESS ANALYTICS, 2e provides students the opportunity to build valuable skills that are in high demand by today’s businesses. Excellent examples and visuals help illustrate data and results for each topic. Step-by-step instructions for various software programs help you perform the analyses discussed. You will find practical, relevant problems at a variety of difficulty levels to help you learn and succeed in your course.

 

Table of Content:

  1. Chapter 1: Introduction
  2. 1.1 Decision Making
  3. 1.2 Business Analytics Defined
  4. 1.3 A Categorization of Analytical Methods and Models
  5. Descriptive Analytics
  6. Predictive Analytics
  7. Prescriptive Analytics
  8. 1.4 Big Data
  9. Volume
  10. Velocity
  11. Variety
  12. Veracity
  13. 1.5 Business Analytics in Practice
  14. Financial Analytics
  15. Human Resource (HR) Analytics
  16. Marketing Analytics
  17. Health Care Analytics
  18. Supply-Chain Analytics
  19. Analytics for Government and Nonprofits
  20. Sports Analytics
  21. Web Analytics
  22. Summary
  23. Glossary
  24. Chapter 2: Descriptive Statistics
  25. Analytics in Action: U.S. Census Bureau
  26. 2.1 Overview of Using Data: Definitions and Goals
  27. 2.2 Types of Data
  28. Population and Sample Data
  29. Quantitative and Categorical Data
  30. Cross-Sectional and Time Series Data
  31. Sources of Data
  32. 2.3 Modifying Data in Excel
  33. Sorting and Filtering Data in Excel
  34. Conditional Formatting of Data in Excel
  35. 2.4 Creating Distributions from Data
  36. Frequency Distributions for Categorical Data
  37. Relative Frequency and Percent Frequency Distributions
  38. Frequency Distributions for Quantitative Data
  39. Histograms
  40. Cumulative Distributions
  41. 2.5 Measures of Location
  42. Mean (Arithmetic Mean)
  43. Median
  44. Mode
  45. Geometric Mean
  46. 2.6 Measures of Variability
  47. Range
  48. Variance
  49. Standard Deviation
  50. Coefficient of Variation
  51. 2.7 Analyzing Distributions
  52. Percentiles
  53. Quartiles
  54. z-Scores
  55. Empirical Rule
  56. Identifying Outliers
  57. Box Plots
  58. 2.8 Measures of Association Between Two Variables
  59. Scatter Charts
  60. Covariance
  61. Correlation Coefficient
  62. Summary
  63. Glossary
  64. Problems
  65. Case Problem: Heavenly Chocolates Web Site Transactions
  66. Appendix 2.1 Creating Box Plots with XLMiner
  67. Chapter 3: Data Visualization
  68. Analytics in Action: Cincinnati Zoo & Botanical Garden
  69. 3.1 Overview of Data Visualization
  70. Effective Design Techniques
  71. 3.2 Tables
  72. Table Design Principles
  73. Crosstabulation
  74. PivotTables in Excel
  75. Recommended PivotTables in Excel
  76. 3.3 Charts
  77. Scatter Charts
  78. Recommended Charts in Excel
  79. Line Charts
  80. Bar Charts and Column Charts
  81. A Note on Pie Charts and Three-Dimensional Charts
  82. Bubble Charts
  83. Heat Maps
  84. Additional Charts for Multiple Variables
  85. PivotCharts in Excel
  86. 3.4 Advanced Data Visualization
  87. Advanced Charts
  88. Geographic Information Systems Charts
  89. 3.5 Data Dashboards
  90. Principles of Effective Data Dashboards
  91. Applications of Data Dashboards
  92. Summary
  93. Glossary
  94. Problems
  95. Case Problem: All-Time Movie Box-Office Data
  96. Appendix 3.1 Creating a Scatter-Chart Matrix and a Parallel-Coordinates Plot with XLMiner
  97. Chapter 4: Descriptive Data Mining
  98. Analytics in Action: Advice from a Machine
  99. 4.1 Data Preparation
  100. Treatment of Missing Data
  101. Identification of Outliers and Erroneous Data
  102. Variable Representation
  103. 4.2 Cluster Analysis
  104. Measuring Similarity Between Observations
  105. Hierarchical Clustering
  106. k-Means Clustering
  107. Hierarchical Clustering Versus k-Means Clustering
  108. 4.3 Association Rules
  109. Evaluating Association Rules
  110. Summary
  111. Glossary
  112. Problems
  113. Case Problem: Know Thy Customer
  114. Appendix 4.1 Hierarchical Clustering with XLMiner
  115. Appendix 4.2 k-Means Clustering with XLMiner
  116. Appendix 4.3 Association Rules with XLMiner
  117. Chapter 5: Probability: An Introduction to Modeling Uncertainty
  118. Analytics in Action: National Aeronautics and Space Administration
  119. 5.1 Events and Probabilities
  120. 5.2 Some Basic Relationships of Probability
  121. Complement of an Event
  122. Addition Law
  123. 5.3 Conditional Probability
  124. Independent Events
  125. Multiplication Law
  126. Bayes’ Theorem
  127. 5.4 Random Variables
  128. Discrete Random Variables
  129. Continuous Random Variables
  130. 5.5 Discrete Probability Distributions
  131. Custom Discrete Probability Distribution
  132. Expected Value and Variance
  133. Discrete Uniform Probability Distribution
  134. Binomial Probability Distribution
  135. Poisson Probability Distribution
  136. 5.6 Continuous Probability Distributions
  137. Uniform Probability Distribution
  138. Triangular Probability Distribution
  139. Normal Probability Distribution
  140. Exponential Probability Distribution
  141. Summary
  142. Glossary
  143. Problems
  144. Case Problem: Hamilton County Judges
  145. Chapter 6: Statistical Inference
  146. Analytics in Action: John Morrell & Company
  147. 6.1 Selecting a Sample
  148. Sampling from a Finite Population
  149. Sampling from an Infinite Population
  150. 6.2 Point Estimation
  151. Practical Advice
  152. 6.3 Sampling Distributions
  153. Sampling Distribution of x
  154. Sampling Distribution of p
  155. 6.4 Interval Estimation
  156. Interval Estimation of the Population Mean
  157. Interval Estimation of the Population Proportion
  158. 6.5 Hypothesis Tests
  159. Developing Null and Alternative Hypotheses
  160. Type I and Type II Errors
  161. Hypothesis Test of the Population Mean
  162. Hypothesis Test of the Population Proportion
  163. Big Data, Statistical Inference, and Practical Significance
  164. Summary
  165. Glossary
  166. Problems
  167. Case Problem 1: Young Professional Magazine
  168. Case Problem 2: Quality Associates, Inc
  169. Chapter 7: Linear Regression
  170. Analytics in Action: Alliance Data Systems
  171. 7.1 Simple Linear Regression Model
  172. Regression Model
  173. Estimated Regression Equation
  174. 7.2 Least Squares Method
  175. Least Squares Estimates of the Regression Parameters
  176. Using Excel’s Chart Tools to Compute the Estimated Regression Equation
  177. 7.3 Assessing the Fit of the Simple Linear Regression Model
  178. The Sums of Squares
  179. The Coefficient of Determination
  180. Using Excel’s Chart Tools to Compute the Coefficient of Determination
  181. 7.4 The Multiple Regression Model
  182. Regression Model
  183. Estimated Multiple Regression Equation
  184. Least Squares Method and Multiple Regression
  185. Butler Trucking Company and Multiple Regression
  186. Using Excel’s Regression Tool to Develop the Estimated Multiple Regression Equation
  187. 7.5 Inference and Regression
  188. Conditions Necessary for Valid Inference in the Least Squares Regression Model
  189. Testing Individual Regression Parameters
  190. Addressing Nonsignificant Independent Variables
  191. Multicollinearity
  192. Inference and Very Large Samples
  193. 7.6 Categorical Independent Variables
  194. Butler Trucking Company and Rush Hour
  195. Interpreting the Parameters
  196. More Complex Categorical Variables
  197. 7.7 Modeling Nonlinear Relationships
  198. Quadratic Regression Models
  199. Piecewise Linear Regression Models
  200. Interaction Between Independent Variables
  201. 7.8 Model Fitting
  202. Variable Selection Procedures
  203. Overfitting
  204. Summary
  205. Glossary
  206. Problems
  207. Case Problem: Alumni Giving
  208. Appendix 7.1 Regression with XLMiner
  209. Chapter 8: Time Series Analysis and Forecasting
  210. Analytics in Action: ACCO Brands
  211. 8.1 Time Series Patterns
  212. Horizontal Pattern
  213. Trend Pattern
  214. Seasonal Pattern
  215. Trend and Seasonal Pattern
  216. Cyclical Pattern
  217. Identifying Time Series Patterns
  218. 8.2 Forecast Accuracy
  219. 8.3 Moving Averages and Exponential Smoothing
  220. Moving Averages
  221. Forecast Accuracy
  222. Exponential Smoothing
  223. Forecast Accuracy
  224. 8.4 Using Regression Analysis for Forecasting
  225. Linear Trend Projection
  226. Seasonality
  227. Seasonality Without Trend
  228. Seasonality with Trend
  229. Using Regression Analysis as a Causal Forecasting Method
  230. Combining Causal Variables with Trend and Seasonality Effects
  231. Considerations in Using Regression in Forecasting
  232. 8.5 Determining the Best Forecasting Model to Use
  233. Summary
  234. Glossary
  235. Problems
  236. Case Problem: Forecasting Food and Beverage Sales
  237. Appendix 8.1 Using Excel Forecast Sheet
  238. Appendix 8.2 Forecasting with XLMiner
  239. Chapter 9: Predictive Data Mining
  240. Analytics in Action: Orbitz
  241. 9.1 Data Sampling
  242. 9.2 Data Partitioning
  243. 9.3 Accuracy Measures
  244. Evaluating the Classification of Categorical Outcomes
  245. Evaluating the Estimation of Continuous Outcomes
  246. 9.4 Logistic Regression
  247. 9.5 k-Nearest Neighbors
  248. Classifying Categorical Outcomes with k-Nearest Neighbors
  249. Estimating Continuous Outcomes with k-Nearest Neighbors
  250. 9.6 Classification and Regression Trees
  251. Classifying Categorical Outcomes with a Classification Tree
  252. Estimating Continuous Outcomes with a Regression Tree
  253. Ensemble Methods
  254. Summary
  255. Glossary
  256. Problems
  257. Case Problem: Grey Code Corporation
  258. Appendix 9.1 Data Partitioning with XLMiner
  259. Appendix 9.2 Logistic Regression Classification with XLMiner
  260. Appendix 9.3 k-Nearest Neighbor Classification and Estimation with XLMiner
  261. Appendix 9.4 Single Classification and Regression Trees with XLMiner
  262. Appendix 9.5 Random Forests of Classification or Regression Trees with XLMiner
  263. Chapter 10: Spreadsheet Models
  264. Analytics in Action: Procter & Gamble
  265. 10.1 Building Good Spreadsheet Models
  266. Influence Diagrams
  267. Building a Mathematical Model
  268. Spreadsheet Design and Implementing the Model in a Spreadsheet
  269. 10.2 What-If Analysis
  270. Data Tables
  271. Goal Seek
  272. 10.3 Some Useful Excel Functions for Modeling
  273. SUM and SUMPRODUCT
  274. IF and COUNTIF
  275. VLOOKUP
  276. 10.4 Auditing Spreadsheet Models
  277. Trace Precedents and Dependents
  278. Show Formulas
  279. Evaluate Formulas
  280. Error Checking
  281. Watch Window
  282. Summary
  283. Glossary
  284. Problems
  285. Case Problem: Retirement Plan
  286. Chapter 11: Linear Optimization Models
  287. Analytics in Action: MeadWestvaco Corporation
  288. 11.1 A Simple Maximization Problem
  289. Problem Formulation
  290. Mathematical Model for the Par, Inc. Problem
  291. 11.2 Solving the Par, Inc. Problem
  292. The Geometry of the Par, Inc. Problem
  293. Solving Linear Programs with Excel Solver
  294. 11.3 A Simple Minimization Problem
  295. Problem Formulation
  296. Solution for the M&D Chemicals Problem
  297. 11.4 Special Cases of Linear Program Outcomes
  298. Alternative Optimal Solutions
  299. Infeasibility
  300. Unbounded
  301. 11.5 Sensitivity Analysis
  302. Interpreting Excel Solver Sensitivity Report
  303. 11.6 General Linear Programming Notation and More Examples
  304. Investment Portfolio Selection
  305. Transportation Planning
  306. Advertising Campaign Planning
  307. 11.7 Generating an Alternative Optimal Solution for a Linear Program
  308. Summary
  309. Glossary
  310. Problems
  311. Case Problem: Investment Strategy
  312. Appendix 11.1 Solving Linear Optimization Models Using Analytic Solver Platform
  313. Chapter 12: Integer Linear Optimization Models
  314. Analytics in Action: Petrobras
  315. 12.1 Types of Integer Linear Optimization Models
  316. 12.2 Eastborne Realty, An Example of Integer Optimization
  317. The Geometry of Linear All-Integer Optimization
  318. 12.3 Solving Integer Optimization Problems with Excel Solver
  319. A Cautionary Note About Sensitivity Analysis
  320. 12.4 Applications Involving Binary Variables
  321. Capital Budgeting
  322. Fixed Cost
  323. Bank Location
  324. Product Design and Market Share Optimization
  325. 12.5 Modeling Flexibility Provided by Binary Variables
  326. Multiple-Choice and Mutually Exclusive Constraints
  327. k Out of n Alternatives Constraint
  328. Conditional and Corequisite Constraints
  329. 12.6 Generating Alternatives in Binary Optimization
  330. Summary
  331. Glossary
  332. Problems
  333. Case Problem: Applecore Children’s Clothing
  334. Appendix 12.1 Solving Integer Linear Optimization Problems Using Analytic Solver Platform
  335. Chapter 13 Nonlinear Optimization Models
  336. Analytics in Action: Intercontinental Hotels
  337. 13.1 A Production Application: Par, Inc. Revisited
  338. An Unconstrained Problem
  339. A Constrained Problem
  340. Solving Nonlinear Optimization Models Using Excel Solver
  341. Sensitivity Analysis and Shadow Prices in Nonlinear Models
  342. 13.2 Local and Global Optima
  343. Overcoming Local Optima with Excel Solver
  344. 13.3 A Location Problem
  345. 13.4 Markowitz Portfolio Model
  346. 13.5 Forecasting Adoption of a New Product
  347. Summary
  348. Glossary
  349. Problems
  350. Case Problem: Portfolio Optimization with Transaction Costs
  351. Appendix 13.1 Solving Nonlinear Optimization Problems with Analytic Solver Platform
  352. Chapter 14: Monte Carlo Simulation
  353. Analytics in Action: Cook County Hospital ICU
  354. 14.1 Risk Analysis for Sanotronics LLC
  355. Base-Case Scenario
  356. Worst-Case Scenario
  357. Best-Case Scenario
  358. Sanotronics Spreadsheet Model
  359. Use of Probability Distributions to Represent Random Variables
  360. Generating Values for Random Variables with Excel
  361. Executing Simulation Trials with Excel
  362. Measuring and Analyzing Simulation Output
  363. 14.2 Simulation Modeling for Land Shark Inc
  364. Spreadsheet Model for Land Shark
  365. Generating Values for Land Shark’s Random Variables
  366. Executing Simulation Trials and Analyzing Output
  367. 14.3 Simulation Considerations
  368. Verification and Validation
  369. Advantages and Disadvantages of Using Simulation
  370. Summary
  371. Glossary
  372. Problems
  373. Case Problem: Four Corners
  374. Appendix 14.1 Land Shark Inc. Simulation with Analytic Solver Platform
  375. Appendix 14.2 Distribution Fitting with Analytic Solver Platform
  376. Appendix 14.3 Simulation Optimization with Analytic Solver Platform
  377. Appendix 14.4 Correlating Random Variables with Analytic Solver Platform
  378. Appendix 14.5 Probability Distributions for Random Variables
  379. Chapter 15: Decision Analysis
  380. Analytics in Action: Phytopharm
  381. 15.1 Problem Formulation
  382. Payoff Tables
  383. Decision Trees
  384. 15.2 Decision Analysis Without Probabilities
  385. Optimistic Approach
  386. Conservative Approach
  387. Minimax Regret Approach
  388. 15.3 Decision Analysis with Probabilities
  389. Expected Value Approach
  390. Risk Analysis
  391. Sensitivity Analysis
  392. 15.4 Decision Analysis with Sample Information
  393. Expected Value of Sample Information
  394. Expected Value of Perfect Information
  395. 15.5 Computing Branch Probabilities with Bayes’ Theorem
  396. 15.6 Utility Theory
  397. Utility and Decision Analysis
  398. Utility Functions
  399. Exponential Utility Function
  400. Summary
  401. Glossary
  402. Problems
  403. Case Problem: Property Purchase Strategy
  404. Appendix 15.1 Using Analytic Solver Platform to Create Decision Trees
  405. APPENDIX A: Basics of Excel
  406. APPENDIX B: Database Basics with Microsoft Access
  407. References
  408. Index

 

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