Solution Manual for Forecasting And Predictive Analytics With Forecast X, 7Th Edition by Keating

1. Section 1: Time Series Models
2. Section 2: Demand Planning
3. Section 3: Analytics
4. Quantitative Forecasting Has Become Widely Accepted
5. Forecasting in Business Today
6. Comments from the Field: Post Foods
7. Comments from the Field: Petsafe
8. Comments from the Field: Global Forecasting Issues, Ocean Spray Cranberries
9. Forecasting in the Public and Not-for-Profit Sectors
10. A Police Department
11. The Texas Legislative Board
12. The California Legislative Analysis Office
13. Forecasting and Supply Chain Management
14. Collaborative Forecasting
15. Computer Use and Quantitative Forecasting
16. Qualitative or Subjective Forecasting Methods
17. Sales Force Composites
18. Surveys of Customers and the General Population
19. Jury of Executive Opinion
20. The Delphi Method
21. Some Advantages and Disadvantages of Subjective Methods
22. New-Product Forecasting
23. Using Marketing Research to Aid New-Product Forecasting
24. The Product Life Cycle Concept Aids in New-Product Forecasting
25. Analog Forecasts
26. Test Marketing
27. Product Clinics
28. Type of Product Affects New-Product Forecasting
29. The Bass Model for New-Product Forecasting
30. Forecasting Sales for New Products That Have Short Product Life Cycles
31. A Simple Naive Forecasting Model
32. Evaluating Forecasts
33. Using Multiple Forecasts
34. Sources of Data
35. Forecasting Total New Houses Sold
36. Steps to Better Time Series Forecasts
37. Integrative Case: Forecasting Sales of the Gap
38. Background of the Gap and Gap Sales
39. An Introduction to ForecastX™
40. Forecasting with the ForecastX Wizard™
41. Using the Five Main Tabs on the Opening ForecastX™ Screen
42. Suggested Readings and Web Sites
43. Exercises
44. Chapter 2 The Forecast Process, Data Considerations, and Model Selection
45. Introduction
46. The Forecast Process
47. Trend, Seasonal, and Cyclical Data Patterns
48. Data Patterns and Model Selection
49. A Statistical Review
50. Descriptive Statistics
51. The Normal Distribution
52. The Student’s t-Distribution
53. From Sample to Population: Statistical Inference
54. Hypothesis Testing
55. Correlation
56. Correlograms: Another Method of Data Exploration
57. Total New Houses Sold: Exploratory Data Analysis and Model Selection
58. Business Forecasting: A Process, Not an Application
59. Integrative Case: The Gap
60. Comments from the Field: Anchorage Economic Development Center Secures Time-Saving Forecasting Accuracy
61. Using ForecastXTM to Find Autocorrelation Functions
62. Suggested Readings
63. Exercises
64. Chapter 3 Extrapolation 1. Moving Averages and Exponential Smoothing
65. Moving Averages
66. Simple Exponential Smoothing
67. Holt’s Exponential Smoothing
68. Winters’ Exponential Smoothing
69. The Seasonal Indices
70. Adaptive–Response-Rate Single Exponential Smoothing
71. Using Single, Holt’s, or ADRES Smoothing to Forecast a Seasonal Data Series
72. New-Product Forecasting (Growth Curve Fitting)
73. Gompertz Curve
74. Logistics Curve
75. Bass Model
76. The Bass Model in Action
77. Event Modeling
78. Forecasting Jewelry Sales with Exponential Smoothing
79. Summary
80. Integrative Case: The Gap
81. Using ForecastXTM to Make Exponential Smoothing Forecasts
82. Suggested Readings
83. Exercises
84. Chapter 4 Extrapolation 2. Introduction to Forecasting with Regression Trend Models
85. The Bivariate Regression Model
86. Visualization of Data: An Important Step in Regression Analysis
87. A Process for Regression Forecasting
88. Forecasting with a Simple Linear Trend
89. Using a Causal Regression Model to Forecast
90. A Jewelry Sales Forecast Based on Disposable Personal Income
91. Statistical Evaluation of Regression Models
92. Basic Diagnostic Checks for Evaluating Regression Results
93. Using the Standard Error of the Estimate
94. Serial Correlation
95. Heteroscedasticity
96. Cross-Sectional Forecasting
97. Forecasting Total Houses Sold with Two Bivariate Regression Models
98. Comments from the Field
99. Integrative Case: The Gap
100. Using ForecastXTM to Make Regression Forecasts
101. Further Comments on Using ForecastXTM to Develop Regression Models
102. Suggested Readings
103. Exercises
104. Chapter 5 Explanatory Models 1. Forecasting with Multiple Regression Causal Models
105. The Multiple-Regression Model
106. Initial Considerations When Selecting Independent Variables
107. Developing Multiple-Regression Models
108. A Three-Dimensional Scattergram
109. Statistical Evaluation of Multiple-Regression Models
110. The First Four Quick Checks in Evaluating Multiple-Regression Models
111. Multicollinearity
112. Serial Correlation: An Extended Look
113. Serial Correlation and the Omitted-Variable Problem
114. Alternative-Variable Selection Criteria
115. Accounting for Seasonality in a Multiple-Regression Model
116. Using a Dummy Variable to Account for a Recession
117. Extensions of the Multiple-Regression Model
118. Advice on Using Multiple Regression in Forecasting
119. Independent Variable Selection
120. The Gap Example (With Leading Indicators)
121. Integrative Case: The Gap
122. Using ForecastXTM to Make Multiple-Regression Forecasts
123. Suggested Readings
124. Exercises
125. Appendix: Combining Forecasts (Ensemble Models)
126. Introduction
127. Bias
128. What Kinds of Forecasts Can be Combined?
129. Considerations in Choosing the Weights for Combined Forecasts
130. One Technique for Selecting Weights When Combining Forecasts
131. An Application of the Regression Method for Combining Forecasts
132. Summarizing the Steps for Combining Forecasts
133. Integrative Case: The Gap
134. Using ForecastXTM to Combine Forecasts
135. Suggested Readings
136. Exercises
137. Chapter 6 Explanatory Models 2. Time-Series Decomposition
138. The Basic Time-Series Decomposition Model
139. Deseasonalizing the Data and Finding Seasonal Indices
140. Finding the Long-Term Trend
141. Measuring the Cyclical Component
142. Overview of Business Cycles
143. Business Cycle Indicators
144. The Cycle Factor for Private Housing Starts
145. The Time-Series Decomposition Forecast
146. Forecasting Winter Daily Natural Gas Demand at Vermont Gas Systems
147. Integrative Case: The Gap
148. Using ForecastXTM to Make Time-Series Decomposition Forecasts
149. Suggested Readings
150. Exercises
151. Chapter 7 Explanatory Models 3. ARIMA (Box-Jenkins) Forecasting Models
152. Introduction
153. The Philosophy of Box-Jenkins
154. Moving-Average Models
155. Autoregressive Models
156. Mixed Autoregressive and Moving-Average Models
157. Stationarity
158. The Box-Jenkins Identification Process
159. ARIMA: A Set of Numerical Examples
160. Example 1
161. Example 2
162. Example 3
163. Example 4
164. Forecasting Seasonal Time Series
165. Total Houses Sold
166. ARIMA in Actual Use: Intelligent Transportation Systems
167. Integrative Case: Forecasting Sales of the Gap
168. Overfitting
169. Using ForecastXTM to Make ARIMA (Box-Jenkins) Forecasts
170. Suggested Readings
171. Exercises
172. Appendix: Critical Values of Chi-Square
173. Chapter 8 Predictive Analytics: Helping to Make Sense of Big Data
174. Applying Analytics in Financial Institutions’ Fight Against Fraud
175. Introduction
176. Big Data
177. Analytics
178. Big Data and Its Characteristics
179. “Datafication”
180. Data Mining
181. Database Management
182. Data Mining Versus Database Management
183. Patterns in Data Mining
184. The Tools of Analytics
185. Statistical Forecasting and Data Mining
186. Terminology in Data Mining: Speak Like a Data Miner
187. Correlation
188. Early Uses of Analytics
189. The “Steps” in a Data Mining Process
190. The Data Itself
191. Overfitting
192. Accuracy and Fit (Again)
193. Some Other Data Considerations
194. Sampling/Partitioning
195. Diagnostics (Evaluating Predictive Performance)
196. The Confusion Matrix and Misclassification Rate
197. The Lift Chart
198. The Receiver Operating Curve (ROC) and Area Under the Curve (AUC)
199. What Is to Follow
200. Suggested Readings
201. Exercises
202. Chapter 9 Classification Models: The Most Used Models in Analytics
203. Introduction
204. A Data Mining Classification Example: k-Nearest-Neighbor (kNN)
205. A Business Data Mining Classification Example: k-Nearest-Neighbor (kNN)
206. Classification Trees: A Second Classification Technique
207. A Business Data Mining Example: Classification Trees
208. A Business Data Mining Example: Regression Trees
209. Naive Bayes: A Third Classification Technique
210. Logit: A Fourth Classification Technique
211. Bank Distress
212. Summary
213. Suggested Readings
214. Exercises
215. Chapter 10 Ensemble Models and Clustering
216. Introduction
217. Ensembles
218. Error Due to Bias
219. Error Due to Variance
220. The Case for Boosting and Bagging
221. Bagging
222. Boosting
223. Random Forest®
224. A Bagging Example
225. A Boosting Example
226. A Random Forest (i.e., Tree) Example
227. Clustering
228. Usefulness of Clustering
229. How Clustering Works
230. A Clustering Example (k-Means Clustering)
231. A Hierarchical Clustering Example (Agglomerative Bottom-Up Clustering)
232. Suggested Readings and Web Sites
233. Exercises
234. Chapter 11 Text Mining
235. Introduction
236. Why Turn Text into Numbers?
237. Where to Start—The “Bag of Words” Analysis
238. Newsgroups
239. In Pictures
240. Understanding SVD
241. Back to the Usenet Example
242. A Logistics Regression Classification of the Usenet Postings
243. Natural Language Processing
244. Data Mining and Text Mining Combined
245. Target Leakage
246. Conclusion
247. Suggested Readings
248. Exercises
249. Chapter 12 Forecast/Analytics Implementation
250. Forecasting Involves a Definite Flow
251. The Forecast Process
252. A Nine-Step Forecasting Process
253. Step 1. Specify Objectives
254. Step 2. Determine What to Forecast
255. Step 3. Identify Time Dimensions
256. Step 4. Data Considerations
257. Step 5. Model Selection
258. Step 6. Model Evaluation
259. Step 7. Forecast Preparation
260. Step 8. Forecast Presentation
261. Step 9. Tracking Results
262. Choosing a Forecasting Technique
263. Sales Force Composite (SFC)
264. Customer Surveys (CS)
265. Jury of Executive Opinion (JEO)
266. Delphi Method
267. Naive
268. Moving Averages
269. Simple Exponential Smoothing (SES)
270. Adaptive–Response-Rate Single Exponential Smoothing (ADRES)
271. Holt’s Exponential Smoothing (HES)
272. Winters’ Exponential Smoothing (WES)
273. Regression-Based Trend Models
274. Regression-Based Trend Models with Seasonality
275. Regression Models with Causality
276. Time-Series Decomposition (TSD)
277. ARIMA
278. Data Mining
279. Text Mining
280. Special Forecasting Considerations
281. Event Modeling
282. Combining Forecasts (Ensembles)
283. New-Product Forecasting (NPF)
284. Data Mining
285. Text Mining
286. Using ProcastTM in ForecastXTM to Make Forecasts
287. Suggested Readings
288. Exercises
289. Index

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