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Solution Manual for Business Forecasting, 9/E 9th Edition John E. Hanke, Dean Wichern

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This is completed downloadable of Solution Manual for Business Forecasting, 9/E 9th Edition John E. Hanke, Dean Wichern

Product Details:

  • ISBN-10 ‏ : ‎ 0132301202
  • ISBN-13 ‏ : ‎ 978-0132301206
  • Author: John E. Hanke, Dean Wichern

Written in a simple, straightforward style, Business Forecasting, 9th Edition presents basic statistical techniques using practical business examples to teach readers how to predict long-term forecasts.

 

Table of Content:

  1. CHAPTER 1 Introduction to Forecasting
  2. The History of Forecasting
  3. Is Forecasting Necessary?
  4. Types of Forecasts
  5. Macroeconomic Forecasting Considerations
  6. Choosing a Forecasting Method
  7. Forecasting Steps
  8. Managing the Forecasting Process
  9. Forecasting Software
  10. Online Information
  11. Forecasting Examples
  12. Summary
  13. Case 1-1: Mr. Tux
  14. Case 1-2: Consumer Credit Counseling
  15. Minitab Applications
  16. Excel Applications
  17. References
  18. CHAPTER 2 A Review of Basic Statistical Concepts
  19. Describing Data with Numerical Summaries
  20. Displays of Numerical Information
  21. Probability Distributions
  22. Sampling Distributions
  23. Inference from a Sample
  24. Estimation
  25. Hypothesis Testing
  26. p-Value
  27. Correlation Analysis
  28. Scatter Diagrams
  29. Correlation Coefficient
  30. Fitting a Straight Line
  31. Assessing Normality
  32. Application to Management
  33. Glossary
  34. Key Formulas
  35. Problems
  36. Case 2-1: Alcam Electronics
  37. Case 2-2: Mr. Tux
  38. Case 2-3: Alomega Food Stores
  39. Minitab Applications
  40. Excel Applications
  41. References
  42. CHAPTER 3 Exploring Data Patterns and an Introduction to Forecasting Techniques
  43. Exploring Time Series Data Patterns
  44. Exploring Data Patterns with Autocorrelation Analysis
  45. Are the Data Random?
  46. Do the Data Have a Trend?
  47. Are the Data Seasonal?
  48. Choosing a Forecasting Technique
  49. Forecasting Techniques for Stationary Data
  50. Forecasting Techniques for Data with a Trend
  51. Forecasting Techniques for Seasonal Data
  52. Forecasting Techniques for Cyclical Series
  53. Other Factors to Consider When Choosing a Forecasting Technique
  54. Empirical Evaluation of Forecasting Methods
  55. Measuring Forecast Error
  56. Determining the Adequacy of a Forecasting Technique
  57. Application to Management
  58. Glossary
  59. Key Formulas
  60. Problems
  61. Case 3-1A: Murphy Brothers Furniture
  62. Case 3-1B: Murphy Brothers Furniture
  63. Case 3-2: Mr. Tux
  64. Case 3-3: Consumer Credit Counseling
  65. Case 3-4: Alomega Food Stores
  66. Case 3-5: Surtido Cookies
  67. Minitab Applications
  68. Excel Applications
  69. References
  70. CHAPTER 4 Moving Averages and Smoothing Methods
  71. Naive Models
  72. Forecasting Methods Based on Averaging
  73. Simple Averages
  74. Moving Averages
  75. Double Moving Averages
  76. Exponential Smoothing Methods
  77. Exponential Smoothing Adjusted for Trend: Holt’s Method
  78. Exponential Smoothing Adjusted for Trend and Seasonal Variation:Winter’s Method
  79. Application to Management
  80. Glossary
  81. Key Formulas
  82. Problems
  83. Case 4-1: The Solar Alternative Company
  84. Case 4-2: Mr. Tux
  85. Case 4-3: Consumer Credit Counseling
  86. Case 4-4: Murphy Brothers Furniture
  87. Case 4-5: Five-Year Revenue Projection for Downtown Radiology
  88. Case 4-6: Web Retailer
  89. Case 4-7: Southwest Medical Center
  90. Case 4-8: Surtido Cookies
  91. Minitab Applications
  92. Excel Applications
  93. References
  94. CHAPTER 5 Time Series and Their Components
  95. Decomposition
  96. Trend
  97. Additional Trend Curves
  98. Forecasting Trend
  99. Seasonality
  100. Seasonally Adjusted Data
  101. Cyclical and Irregular Variations
  102. Summary Example
  103. Business Indicators
  104. Forecasting a Seasonal Time Series
  105. The Census II Decomposition Method
  106. Application to Management
  107. Appendix: Price Index
  108. Glossary
  109. Key Formulas
  110. Problems
  111. Case 5-1: The Small Engine Doctor
  112. Case 5-2: Mr. Tux
  113. Case 5-3: Consumer Credit Counseling
  114. Case 5-4: Murphy Brothers Furniture
  115. Case 5-5: AAA Washington
  116. Case 5-6: Alomega Food Stores
  117. Case 5-7: Surtido Cookies
  118. Case 5-8: Southwest Medical Center
  119. Minitab Applications
  120. Excel Applications
  121. References
  122. CHAPTER 6 Simple Linear Regression
  123. Regression Line
  124. Standard Error of the Estimate
  125. Forecasting Y
  126. Decomposition of Variance
  127. Coefficient of Determination
  128. Hypothesis Testing
  129. Analysis of Residuals
  130. Computer Output
  131. Variable Transformations
  132. Growth Curves
  133. Application to Management
  134. Glossary
  135. Key Formulas
  136. Problems
  137. Case 6-1: Tiger Transport
  138. Case 6-2: Butcher Products, Inc.
  139. Case 6-3: Ace Manufacturing
  140. Case 6-4: Mr. Tux
  141. Case 6-5: Consumer Credit Counseling
  142. Case 6-6: AAA Washington
  143. Minitab Applications
  144. Excel Applications
  145. References
  146. CHAPTER 7 Multiple Regression Analysis
  147. Several Predictor Variables
  148. Correlation Matrix
  149. Multiple Regression Model
  150. Statistical Model for Multiple Regression
  151. Interpreting Regression Coefficients
  152. Inference for Multiple Regression Models
  153. Standard Error of the Estimate
  154. Significance of the Regression
  155. Individual Predictor Variables
  156. Forecast of a Future Response
  157. Computer Output
  158. Dummy Variables
  159. Multicollinearity
  160. Selecting the “Best” Regression Equation
  161. All Possible Regressions
  162. Stepwise Regression
  163. Final Notes on Stepwise Regression
  164. Regression Diagnostics and Residual Analysis
  165. Forecasting Caveats
  166. Overfitting
  167. Useful Regression, Large F Ratios
  168. Application to Management
  169. Glossary
  170. Key Formulas
  171. Problems
  172. Case 7-1: The Bond Market
  173. Case 7-2: AAA Washington
  174. Case 7-3: Fantasy Baseball (A)
  175. Case 7-4: Fantasy Baseball (B)
  176. Minitab Applications
  177. Excel Applications
  178. References
  179. CHAPTER 8 Regression with Time Series Data
  180. Time Series Data and the Problem of Autocorrelation
  181. Autocorrelation and the Durbin-Watson Test
  182. Solutions to Autocorrelation Problems
  183. Model Specification Error (Omitting a Variable)
  184. Regression with Differences
  185. Autocorrelated Errors and Generalized Differences
  186. Autoregressive Models
  187. Summary
  188. Time Series Data and the Problem of Heteroscedasticity
  189. Using Regression to Forecast Seasonal Data
  190. Econometric Forecasting
  191. Cointegrated Time Series
  192. Application to Management
  193. Glossary
  194. Key Formulas
  195. Problems
  196. Case 8-1: Company of Your Choice
  197. Case 8-2: Business Activity Index for Spokane County
  198. Case 8-3: Restaurant Sales
  199. Case 8-4: Mr. Tux
  200. Case 8-5: Consumer Credit Counseling
  201. Case 8-6: AAA Washington
  202. Case 8-7: Alomega Food Stores
  203. Case 8-8: Surtido Cookies
  204. Case 8-9: Southwest Medical Center
  205. Minitab Applications
  206. Excel Applications
  207. References
  208. CHAPTER 9 The Box-Jenkins (ARIMA) Methodology
  209. Box-Jenkins Methodology
  210. Autoregressive Models
  211. Moving Average Models
  212. Autoregressive Moving Average Models
  213. Summary
  214. Implementing the Model-Building Strategy
  215. Step 1: Model Identification
  216. Step 2: Model Estimation
  217. Step 3: Model Checking
  218. Step 4: Forecasting with the Model
  219. Model-Building Caveats
  220. Model Selection Criteria
  221. ARIMA Models for Seasonal Data
  222. Simple Exponential Smoothing and an ARIMA Model
  223. Advantages and Disadvantages of ARIMA Models
  224. Application to Management
  225. Glossary
  226. Key Formulas
  227. Problems
  228. Case 9-1: Restaurant Sales
  229. Case 9-2: Mr. Tux
  230. Case 9-3: Consumer Credit Counseling
  231. Case 9-4: The Lydia E. Pinkham Medicine Company
  232. Case 9-5: City of College Station
  233. Case 9-6: UPS Air Finance Division
  234. Case 9-7: AAA Washington
  235. Case 9-8: Web Retailer
  236. Case 9-9: Surtido Cookies
  237. Case 9-10: Southwest Medical Center
  238. Minitab Applications
  239. References
  240. CHAPTER 10 Judgmental Forecasting and Forecast Adjustments
  241. Judgmental Forecasting
  242. The Delphi Method
  243. Scenario Writing
  244. Combining Forecasts
  245. Forecasting and Neural Networks
  246. Summary of Judgmental Forecasting
  247. Other Tools Useful in Making Judgments About the Future
  248. Key Formulas
  249. Problems
  250. Case 10-1: Golden Gardens Restaurant
  251. Case 10-2: Alomega Food Stores
  252. Case 10-3: The Lydia E. Pinkham Medicine Company
  253. References
  254. CHAPTER 11 Managing the Forecasting Process
  255. The Forecasting Process
  256. Monitoring Forecasts
  257. Forecasting Steps Reviewed
  258. Forecasting Responsibility
  259. Forecasting Costs
  260. Forecasting and Management Information Systems
  261. Selling Management on Forecasting
  262. The Future of Forecasting
  263. Problems
  264. Case 11-1: Boundary Electronics
  265. Case 11-2: Busby Associates
  266. Case 11-3: Consumer Credit Counseling
  267. Case 11-4: Mr. Tux
  268. Case 11-5: Alomega Food Stores
  269. Case 11-6: Southwest Medical Center
  270. References
  271. APPENDIX A: Data for Case 7-1
  272. APPENDIX B: Tables
  273. Table B-1 Individual Terms of the Binomial Distribution
  274. Table B-2 Areas for Standard Normal Probability Distribution
  275. Table B-3 Critical Values of t
  276. Table B-4 Critical Values of Chi-Square
  277. Table B-5 F Distribution
  278. Table B-6 Durbin-Watson Test Bounds
  279. APPENDIX C: Data Sets and Databases
  280. Index
  281. A
  282. B
  283. C
  284. D
  285. E
  286. F
  287. G
  288. H
  289. I
  290. J
  291. K
  292. L
  293. M
  294. N
  295. O
  296. P
  297. Q
  298. R
  299. S
  300. T
  301. U
  302. V
  303. W
  304. X
  305. Y
  306. Z

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