1. Preface
2. Chapter 1: Introduction to Business Analytics
3. 1-1 Introduction
4. 1-2 Overview Of The Book
5. 1-2a The Methods
6. 1-2b The Software
7. 1-3 Modeling And Models
8. 1-3a Graphical Models
9. 1-3b Algebraic Models
10. 1-3c Spreadsheet Models
11. 1-3d A Seven-Step Modeling Process
12. 1-4 Conclusion
13. Part 1: Exploring Data
14. Chapter 2: Describing the Distribution of a Single Variable
15. 2-1 Introduction
16. 2-2 Basic Concepts
17. 2-2a Populations and Samples
18. 2-2b Data Sets, Variables, and Observations
19. 2-2c Types of Data
20. 2-3 Descriptive Measures For Categorical Variables
21. 2-4 Descriptive Measures For Numerical Variables
22. 2-4a Numerical Summary Measures
23. 2-4b Numerical Summary Measures with StatTools
24. 2-4c Analysis ToolPak Add-In
25. 2-4d Charts for Numerical Variables
26. 2-5 Time Series Data
27. 2-6 Outliers And Missing Values
28. 2-6a Outliers
29. 2-6b Missing Values
30. 2-7 Excel Tables For Filtering, Sorting, And Summarizing
31. 2-8 Conclusion
32. Summary of Key Terms
33. Chapter 3: Finding Relationships among Variables
34. 3-1 Introduction
35. 3-2 Relationships Among Categorical Variables
36. 3-3 Relationships Among Categorical Variables And A Numerical Variable
37. 3-3a Stacked and Unstacked Formats
38. 3-4 Relationships Among Numerical Variables
39. 3-4a Scatterplots
40. 3-4b Correlation and Covariance
41. 3-5 Pivot Tables
42. 3-6 Conclusion
43. Part 2: Probability and Decision Making under Uncertainty
44. Chapter 4: Probability and Probability Distributions
45. 4-1 Introduction
46. 4-2 Probability Essentials
47. 4-2a Rule of Complements
48. 4-2b Addition Rule
49. 4-2c Conditional Probability and the Multiplication Rule
50. 4-2d Probabilistic Independence
51. 4-2e Equally Likely Events
52. 4-2f Subjective Versus Objective Probabilities
53. 4-3 Probability Distribution Of A Single Random Variable
54. 4-3a Summary Measures of a Probability Distribution
55. 4-3b Conditional Mean and Variance
56. 4-4 Introduction To Simulation
57. 4-5 Conclusion
58. Chapter 5: Normal, Binomial, Poisson, and Exponential Distributions
59. 5-1 Introduction
60. 5-2 The Normal Distribution
61. 5-2a Continuous Distributions and Density Functions
62. 5-2b The Normal Density
63. 5-2c Standardizing: Z-Values
64. 5-2d Normal Tables and Z-Values
65. 5-2e Normal Calculations in Excel
66. 5-2f Empirical Rules Revisited
67. 5-2g Weighted Sums of Normal Random Variables
68. 5-3 Applications Of The Normal Distribution
69. 5-4 The Binomial Distribution
70. 5-4a Mean and Standard Deviation of the Binomial Distribution
71. 5-4b The Binomial Distribution in the Context of Sampling
72. 5-4c The Normal Approximation to the Binomial
73. 5-5 Applications Of The Binomial Distribution
74. 5-6 The Poisson And Exponential Distributions
75. 5-6a The Poisson Distribution
76. 5-6b The Exponential Distribution
77. 5-7 Conclusion
78. Chapter 6: Decision Making Under Uncertainty
79. 6-1 Introduction
80. 6-2 Elements Of Decision Analysis
81. 6-2a Identifying the Problem
82. 6-2b Possible Decisions
83. 6-2c Possible Outcomes
84. 6-2d Probabilities of Outcomes
85. 6-2e Payoffs and Costs
86. 6-2f Decision Criterion
87. 6-2g More about the EMV Criterion
88. 6-2h Decision Trees
89. 6-3 One-stage Decision Problems
90. 6-4 The PrecisionTree Add-in
91. 6-5 Multistage Decision Problems
92. 6-6 The Role Of Risk Aversion
93. 6-6a Utility Functions
94. 6-6b Exponential Utility
95. 6-6c Certainty Equivalents
96. 6-6d Is Expected Utility Maximization Used?
97. 6-7 Conclusion
98. Part 3: Statistical Inference
99. Chapter 7: Sampling and Sampling Distributions
100. 7-1 Introduction
101. 7-2 Sampling Terminology
102. 7-3 Methods For Selecting Random Samples
103. 7-3a Simple Random Sampling
104. 7-3b Systematic Sampling
105. 7-3c Stratified Sampling
106. 7-3d Cluster Sampling
107. 7-3e Multistage Sampling Schemes
108. 7-4 Introduction To Estimation
109. 7-4a Sources of Estimation Error
110. 7-4b Key Terms in Sampling
111. 7-4c Sampling Distribution of the Sample Mean
112. 7-4d The Central Limit Theorem
113. 7-4e Sample Size Selection
114. 7-4f Summary of Key Ideas for Simple Random Sampling
115. 7-5 Conclusion
116. Chapter 8: Confidence Interval Estimation
117. 8-1 Introduction
118. 8-2 Sampling Distributions
119. 8-2a The t Distribution
120. 8-2b Other Sampling Distributions
121. 8-4 Confidence Interval For A Total
122. 8-5 Confidence Interval For A Proportion
123. 8-6 Confidence Interval For A Standard Deviation
124. 8-7 Confidence Interval For The Difference Between Means
125. 8-7a Independent Samples
126. 8-7b Paired Samples
127. 8-8 Confidence Interval For The Difference Between Proportions
128. 8-9 Sample Size Selection
129. 8-9a Sample Size Selection for Estimation of the Mean
130. 8-9b Sample Size Selection for Estimation of Other Parameters
131. 8-10 Conclusion
132. Summary of Key Terms
133. Chapter 9: Hypothesis Testing
134. 9-1 Introduction
135. 9-2 Concepts In Hypothesis Testing
136. 9-2a Null and Alternative Hypotheses
137. 9-2b One-Tailed Versus Two-Tailed Tests
138. 9-2c Types of Errors
139. 9-2d Significance Level and Rejection Region
140. 9-2e Significance from p-values
141. 9-2f Type II Errors and Power
142. 9-2g Hypothesis Tests and Confidence Intervals
143. 9-2h Practical versus Statistical Significance
144. 9-3 Hypothesis Tests For A Population Mean
145. 9-4 Hypothesis Tests For Other Parameters
146. 9-4a Hypothesis Tests for a Population Proportion
147. 9-4b Hypothesis Tests for Differences between Population Means
148. 9-4c Hypothesis Test for Equal Population Variances
149. 9-4d Hypothesis Tests for Differences between Population Proportions
150. 9-5 Tests For Normality
151. 9-6 Chi-square Test For Independence
152. 9-7 Conclusion
153. Part 4: Regression Analysis and Time Series Forecasting
154. Chapter 10: Regression Analysis: Estimating Relationships
155. 10-1 Introduction
156. 10-2 Scatterplots: Graphing Relationships
157. 10-2a Linear versus Nonlinear Relationships
158. 10-2b Outliers
159. 10-2c Unequal Variance
160. 10-2d No Relationship
161. 10-3 Correlations: Indicators Of Linear Relationships
162. 10-4 Simple Linear Regression
163. 10-4a Least Squares Estimation
164. 10-4b Standard Error of Estimate
165. 10-4c The Percentage of Variation Explained: R-Square
166. 10-5 Multiple Regression
167. 10-5a Interpretation of Regression Coefficients
168. 10-5b Interpretation of Standard Error of Estimate and R-Square
169. 10-6 Modeling Possibilities
170. 10-6a Dummy Variables
171. 10-6b Interaction Variables
172. 10-6c Nonlinear Transformations
173. 10-7 Validation of the Fit
174. 10-8 Conclusion
175. Chapter 11: Regression Analysis: Statistical Inference
176. 11-1 Introduction
177. 11-2 The Statistical Model
178. 11-3 Inferences About The Regression Coefficients
179. 11-3a Sampling Distribution of the Regression Coefficients
180. 11-3b Hypothesis Tests for the Regression Coefficients and p-Values
181. 11-3c A Test for the Overall Fit: The ANOVA Table
182. 11-4 Multicollinearity
183. 11-5 Include/Exclude Decisions
184. 11-6 Stepwise Regression
185. 11-7 Outliers
186. 11-8 Violations Of Regression Assumptions
187. 11-8a Nonconstant Error Variance
188. 11-8b Nonnormality of Residuals
189. 11-8c Autocorrelated Residuals
190. 11-9 Prediction
191. 11-10 Conclusion
192. Chapter 12: Time Series Analysis and Forecasting
193. 12-1 Introduction
194. 12-2 Forecasting Methods: An Overview
195. 12-2a Extrapolation Models
196. 12-2b Econometric Models
197. 12-2c Combining Forecasts
198. 12-2d Components of Time Series Data
199. 12-2e Measures of Accuracy
200. 12-3 Testing For Randomness
201. 12-3a The Runs Test
202. 12-3b Autocorrelation
203. 12-4 Regression-based Trend Models
204. 12-4a Linear Trend
205. 12-4b Exponential Trend
206. 12-5 The Random Walk Model
207. 12-6 Moving Averages Forecasts
208. 12-7 Exponential Smoothing Forecasts
209. 12-7a Simple Exponential Smoothing
210. 12-7b Holt’s Model for Trend
211. 12-8 Seasonal Models
212. 12-8a Winters’ Exponential Smoothing Model
213. 12-8b Deseasonalizing: The Ratio-to-Moving-Averages Method
214. 12-8c Estimating Seasonality with Regression
215. 12-9 Conclusion
216. Part 5: Optimization and Simulation Modeling
217. Chapter 13: Introduction to Optimization Modeling
218. 13-1 Introduction
219. 13-2 Introduction To Optimization
220. 13-3 A Two-variable Product Mix Model
221. 13-4 Sensitivity Analysis
222. 13-4a Solver’s Sensitivity Report
223. 13-4b SolverTable Add-In
224. 13-4c Comparison of Solver’s Sensitivity Report and SolverTable
225. 13-5 Properties Of Linear Models
226. 13-6 Infeasibility And Unboundedness
227. 13-7 A Larger Product Mix Model
228. 13-8 A Multiperiod Production Model
229. 13-9 A Comparison Of Algebraic And Spreadsheet Models
230. 13-10 A Decision Support System
231. 13-11 Conclusion
232. Summary of Key Terms
233. Summary of Key Terms (Continued )
234. Chapter 14: Optimization Models
235. 14-1 Introduction
236. 14-2 Employee Scheduling Models
237. 14-3 Blending Models
238. 14-4 Logistics Models
239. 14-4a Transportation Models
240. 14-4b Other Logistics Models
241. 14-5 Aggregate Planning Models
242. 14-6 Financial Models
243. 14-7 Integer Optimization Models
244. 14-7a Capital Budgeting Models
245. 14-7b Fixed-Cost Models
246. 14-7c Set-Covering Models
247. 14-8 Nonlinear Optimization Models
248. 14-8a Basic Ideas of Nonlinear Optimization
249. 14-8b Managerial Economics Models
250. 14-8c Portfolio Optimization Models
251. 14-9 Conclusion
252. Chapter 15: Introduction to Simulation Modeling
253. 15-1 Introduction
254. 15-2 Probability Distributions For Input Variables
255. 15-2a Types of Probability Distributions
256. 15-2b Common Probability Distributions
257. 15-2c Using @RISK to Explore Probability Distributions
258. 15-3 Simulation And The Flaw Of Averages
259. 15-4 Simulation With Built-in Excel Tools
260. 15-5 Introduction To @RISK
261. 15-5a @RISK Features
262. 15-5b Loading @RISK
263. 15-5c @RISK Models with a Single Random Input Variable
264. 15-5d Some Limitations of @RISK
265. 15-5e @RISK Models with Several Random Input Variables
266. 15-6 The Effects Of Input Distributions On Results
267. 15-6a Effect of the Shape of the Input Distribution(s)
268. 15-6b Effect of Correlated Input Variables
269. 15-7 Conclusion
270. Chapter 16: Simulation Models
271. 16-1 Introduction
272. 16-2 Operations Models
273. 16-2a Bidding for Contracts
274. 16-2b Warranty Costs
275. 16-2c Drug Production with Uncertain Yield
276. 16-3 Financial Models
277. 16-3a Financial Planning Models
278. 16-3b Cash Balance Models
279. 16-3c Investment Models
280. 16-4 Marketing Models
281. 16-4a Models of Customer Loyalty
282. 16-4b Marketing and Sales Models
283. 16-5 Simulating Games Of Chance
284. 16-5a Simulating the Game of Craps
285. 16-5b Simulating the NCAA Basketball Tournament
286. 16-6 Conclusion
287. Part 6: Advanced Data Analysis
288. Chapter 17: Data Mining
289. 17-1 Introduction
290. 17-2 Data Exploration And Visualization
291. 17-2a Introduction to Relational Databases
292. 17-2b Online Analytical Processing (OLAP)
293. 17-2c Power Pivot and Self-Service BI Tools in Excel
294. 17-2d Visualization Software
295. 17-3 Classification Methods
296. 17-3a Logistic Regression
297. 17-3b Neural Networks
298. 17-3c Naïve Bayes
299. 17-3d Classification Trees
300. 17-3e Measures of Classification Accuracy
301. 17-3f Classification with Rare Events
302. 17-4 Clustering
303. 17-5 Conclusion
304. Part 7: Bonus Online Material
305. Chapter 18: Importing Data into Excel
306. 18-1 Introduction
307. 18-2 Rearranging Excel Data
308. 18-3 Importing Text Data
309. 18-4 Importing Data Into Excel
310. 18-4a Importing from Access with Old Tools
311. 18-4b Importing from Access with Power Query
312. 18.4c Using Microsoft Query
313. 18.4d SQL Statements and M
314. 18-4e Web Queries
315. 18.5 Cleansing Data
316. 18.6 Conclusion
317. Chapter 19: Analysis of Variance and Experimental Design
318. 19-1 Introduction
319. 19-2 One-way ANOVA
320. 19-2a The Equal-Means Test
321. 19-2b Confidence Intervals for Differences between Means
322. 19-2c Using a Logarithmic Transformation
323. 19-3 Using Regression To Perform ANOVA
324. 19-4 The Multiple Comparison Problem
325. 19-5 Two-way ANOVA
326. 19-5a Confidence Intervals for Contrasts
327. 19-5b Assumptions of Two-Way ANOVA
328. 19-6 More About Experimental Design
329. 19-6a Randomization
330. 19-6b Blocking
331. 19-6c Incomplete Designs
332. 19-7 Conclusion
333. Chapter 20: Statistical Process Control
334. 20-1 Introduction
335. 20-2 Deming’s 14 Points
336. 20-3 Introduction To Control Charts
337. 20-4 Control Charts For Variables
338. 20-4a Control Charts and Hypothesis Testing
339. 20-4b Other Out-of-Control Indications
340. 20-4c Rational Subsamples
341. 20-4d Deming’s Funnel Experiment and Tampering
342. 20-4e Control Charts in the Service Industry
343. 20-5 Control Charts For Attributes
344. 20-5a The p Chart
345. 20-5b The Red Bead Experiment
346. 20-6 Process Capability
347. 20-6a Process Capability Indexes
348. 20-6b More on Motorola and 6-sigma
349. 20-7 Conclusion
350. Appendix A: Statistical Reporting
351. A-1 Introduction
352. A-2 Suggestions For Good Statistical Reporting
353. A-2a Planning
354. A-2b Developing a Report
355. A-2c Be Clear
356. A-2d Be Concise
357. A-2e Be Precise
358. A-3 Examples Of Statistical Reports
359. A-4 Conclusion
360. References
361. Index

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