Business forecasting:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Upper Saddle River, NJ
Prentice Hall
2001
|
| Ausgabe: | 7. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIV, 498 S. graph. Darst. |
| ISBN: | 0130878103 |
Internformat
MARC
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| 100 | 1 | |a Hanke, John E. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Business forecasting |c John E. Hanke ; Arthur G. Reitsch ; Dean W. Wichern |
| 250 | |a 7. ed. | ||
| 264 | 1 | |a Upper Saddle River, NJ |b Prentice Hall |c 2001 | |
| 300 | |a XIV, 498 S. |b graph. Darst. | ||
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| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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| 650 | 7 | |a Prognoses |2 gtt | |
| 650 | 4 | |a Pronóstico de los negocios | |
| 650 | 4 | |a Prévision commerciale | |
| 650 | 7 | |a Tijdreeksen |2 gtt | |
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Datensatz im Suchindex
| _version_ | 1804128447005458432 |
|---|---|
| adam_text | Contents
Preface xiii
CHAPTER 1 Introduction to Forecasting 1
History of Forecasting 1
Need for Forecasting 1
Types of Forecasts 3
Macroeconomic Forecasting 4
Choosing a Forecasting Method 4
Forecasting Steps 4
Managing the Forecasting Process 6
Computer Forecasting Packages 6
Forecasting Example 7
Summary 8
Case Study 1.1: Mr. Tux 9
Case Study 1.2: Consumer Credit Counseling 9
Minitab Applications 10
Excel Applications 11
References and Selected Bibliography 12
CHAPTER 2 A Review of Basic Statistical Concepts 13
Describing Data with Numerical Summaries 13
Displays of Numerical Information 17
Probability Distributions 20
Sampling Distributions 23
Inference from a Sample 25
Estimation 25
Hypothesis Testing 26
V
Correlation Analysis 29
Scatter Diagrams 29
Correlation Coefficient 32
Fitting a Straight Line 34
Assessing Normality 38
Application to Management 39
Glossary 40
Key Formulas 40
Problems 41
Case Study 2.1: Alcam Electronics 46
Case Study 2.2: Mr. Tux 47
Case Study 2.3: Alomega Food Stores 48
Minitab Applications 49
Excel Applications 51
References and Selected Bibliography 52
CHAPTER 3 Exploring Data Patterns and Choosing
a Forecasting Technique 53
Exploring Time Series Data Patterns 54
Exploring Data Patterns with Autocorrelation Analysis 56
Are the Data Random? 61
Do the Data Have a Trend? 62
Are the Data Seasonal? 68
Choosing a Forecasting Technique 69
Forecasting Techniques for Stationary Data 70
Forecasting Techniques for Data with a Trend 71
Forecasting Techniques for Data with Seasonality 71
Forecasting Techniques for Cyclical Series 72
Other Factors to Consider When Choosing a Forecasting Technique 72
Empirical Evaluation of Forecasting Methods 73
Measuring Forecasting Error 74
Determining the Adequacy of a Forecasting Technique 77
Application to Management 78
Glossary 79
Key Formulas 80
Problems 81
Case Study 3.1: Murphy Brothers Furniture 85
Case Study 3.2: Mr. Tux 87
Case Study 3.3: Consumer Credit Counseling 88
Case Study 3.4: Alomega Food Stores 89
Minitab Applications 89
Excel Applications 91
References and Selected Bibliography 93
vi
CHAPTER 4 Moving Averages and Smoothing Methods 95
Naive Models 96
Forecasting Methods Based on Averaging 99
Simple Averages 99
Moving Averages 101
Double Moving Averages 104
Exponential Smoothing Methods 107
Exponential Smoothing Adjusted for Trend: Holt s Method 114
Exponential Smoothing Adjusted for Trend and Seasonal Variation:
Winters Method 117
Application to Management 123
Glossary 124
Key Formulas 124
Problems 126
Case Study 4.1: The Solar Alternative Company 131
Case Study 4.2: Mr. Tux 132
Case Study 4.3: Consumer Credit Counseling 133
Case Study 4.4: Five Year Revenue Projection
for Downtown Radiology 133
Minitab Applications 139
Excel Applications 141
References and Selected Bibliography 142
CHAPTER 5 Time Series and Their Components 143
Decomposition 144
Trend 147
Additional Trend Curves 150
Forecasting Trend 153
Seasonality 153
Seasonally Adjusted Data 158
Cyclical and Irregular Variations 158
Forecasting a Seasonal Time Series 164
The Census II Decomposition Method 165
Application to Management 167
Appendix: Price Index 168
Glossary 170
Key Formulas 170
Problems 171
Case Study 5.1: The Small Engine Doctor 178
Case Study 5.2: Mr. Tux 179
Case Study 5.3: Consumer Credit Counseling 183
Case Study 5.4: AAA Washington 183
vii
Case Study 5.5: Alomega Food Stores 185
Minitab Applications 187
Excel Applications 189
References and Selected Bibliography 192
CHAPTER 6 Simple Linear Regression 193
Regression Line 193
Standard Error of the Estimate 198
Forecasting Y 199
Decomposition of Variance 202
Coefficient of Determination 205
Hypothesis Testing 208
Analysis of Residuals 210
Computer Output 213
Variable Transformations 215
Application to Management 219
Glossary 221
Key Formulas 221
Problems 222
Case Study 6.1: Tiger Transport 231
Case Study 6.2: Butcher Products, Inc. 232
Case Study 6.3: Ace Manufacturing 234
Case Study 6.4: Mr. Tux 235
Case Study 6.5: Consumer Credit Counseling 235
Minitab Applications 236
Excel Applications 238
References and Selected Bibliography 240
CHAPTER 7 Multiple Regression Analysis 241
Several Predictor Variables 241
Correlation Matrix 242
Multiple Regression Model 243
Statistical Model for Multiple Regression 244
Interpreting Regression Coefficients 245
Inference for Multiple Regression Models 246
Standard Error of the Estimate 247
Significance of the Regression 248
Individual Predictor Variable 250
Forecast of a Future Response 251
Computer Output 251
Dummy Variables 252
Multicollinearity 256
viii
Selecting the Best Regression Equation 259
All Possible Regressions 261
Stepwise Regression 263
Final Notes on Stepwise Regression 266
Regression Diagnostics and Residual Analysis 266
Forecasting Caveats 268
Overfitting 268
Useful Regressions, Large F Ratios 269
Application to Management 269
Glossary 271
Key Formulas 271
Problems 272
Case Study 7.1: The Bond Market 280
Case Study 7.2: Fantasy Baseball (A) 282
Case Study 7.3: Fantasy Baseball (B) 288
Minitab Applications 291
Excel Applications 292
References and Selected Bibliography 293
CHAPTER 8 Regression with Time Series Data 294
Time Series Data and the Problem of Autocorrelation 294
Durbin Watson Test for Serial Correlation 298
Solutions to Autocorrelation Problems 301
Model Specification Error (Omitting a Variable) 302
Regression with Differences 304
Generalized Differences and an Iterative Approach 309
Autoregressive Models 312
Time Series Data and the Problem of Heteroscedasticity 313
Using Regression to Forecast Seasonal Data 316
Econometric Forecasting 319
Application to Management 320
Glossary 320
Key Formulas 320
Problems 322
Case Study 8.1: Company of Your Choice 329
Case Study 8.2: Business Activity Index for Spokane County 329
Case Study 8.3: Restaurant Sales 333
Case Study 8.4: Mr. Tux 335
Case Study 8.5: Consumer Credit Counseling 337
Case Study 8.6: AAA Washington 339
Case Study 8.7: Alomega Food Stores 341
Minitab Applications 342
ix
Excel Applications 343
References and Selected Bibliography 345
CHAPTER 9 The Box Jenkins (ARIMA) Methodology 346
Box Jenkins Methodology 346
Autoregressive Models 351
Moving Average Models 352
Autoregressive Moving Average Models 354
Summary 354
Implementing the Model Building Strategy 354
Step 1: Model Identification 354
Step 2: Model Estimation 356
Step 3: Model Checking 357
Step 4: Forecasting with the Model 358
Final Comments 377
Model Selection Criteria 377
Models for Seasonal Data 379
Simple Exponential Smoothing and an ARIMA Model 391
Advantages and Disadvantages of ARIMA Models 391
Application to Management 392
Glossary 393
Key Formulas 393
Problems 394
Case Study 9.1: Restaurant Sales 404
Case Study 9.2: Mr. Tux 405
Case Study 9.3: Consumer Credit Counseling 407
Case Study 9.4: The Lydia E. Pinkham Medicine Company 407
Case Study 9.5: City of College Station 410
Case Study 9.6: UPS Air Finance Division 413
Minitab Applications 416
Excel Applications 418
References and Selected Bibliography 419
CHAPTER 10 Judgmental Elements in Forecasting 421
Growth Curves 422
The Delphi Method 424
Scenario Writing 425
Combining Forecasts 426
Forecasting and Neural Networks 427
Summary of Judgmental Forecasting 429
Other Techniques Useful in Forecasting 430
Key Formulas 433
X
Case Study 10.1 Golden Gardens Restaurant 434
Case Study 10.2 The Lydia E. Pinkham Medicine Company Revisited 434
References and Selected Bibliography 437
CHAPTER 11 Managing the Forecasting Process 438
The Forecasting Process 438
Monitoring Forecasts 439
Forecasting Steps Reviewed 443
Forecasting Responsibility 444
Forecasting Costs 445
Forecasting and the MIS System 445
Selling Management on Forecasting 446
The Future of Forecasting 446
Case Study 11.1: Boundary Electronics 447
Case Study 11.2: Busby Associates 447
Case Study 11.3: Consumer Credit Counseling 451
Case Study 11.4: Mr. Tux 452
Case Study 11.5: Alomega Food Stores 453
References and Selected Bibliography 454
APPENDIX A Derivations 457
APPENDIX B Data for Case Study 7.1 459
APPENDIX C Tables 461
APPENDIX D Data Sets and Databases 472
Index 493
xi
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| spelling | Hanke, John E. Verfasser aut Business forecasting John E. Hanke ; Arthur G. Reitsch ; Dean W. Wichern 7. ed. Upper Saddle River, NJ Prentice Hall 2001 XIV, 498 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Bedrijfsleven gtt Prognoses gtt Pronóstico de los negocios Prévision commerciale Tijdreeksen gtt Business forecasting Statistik (DE-588)4056995-0 gnd rswk-swf Prognose (DE-588)4047390-9 gnd rswk-swf Management (DE-588)4037278-9 gnd rswk-swf Prognoseverfahren (DE-588)4358095-6 gnd rswk-swf Management (DE-588)4037278-9 s Prognoseverfahren (DE-588)4358095-6 s Statistik (DE-588)4056995-0 s DE-604 Prognose (DE-588)4047390-9 s 1\p DE-604 Reitsch, Arthur G. Verfasser aut Wichern, Dean W. Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009317544&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Hanke, John E. Reitsch, Arthur G. Wichern, Dean W. Business forecasting Bedrijfsleven gtt Prognoses gtt Pronóstico de los negocios Prévision commerciale Tijdreeksen gtt Business forecasting Statistik (DE-588)4056995-0 gnd Prognose (DE-588)4047390-9 gnd Management (DE-588)4037278-9 gnd Prognoseverfahren (DE-588)4358095-6 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4047390-9 (DE-588)4037278-9 (DE-588)4358095-6 |
| title | Business forecasting |
| title_auth | Business forecasting |
| title_exact_search | Business forecasting |
| title_full | Business forecasting John E. Hanke ; Arthur G. Reitsch ; Dean W. Wichern |
| title_fullStr | Business forecasting John E. Hanke ; Arthur G. Reitsch ; Dean W. Wichern |
| title_full_unstemmed | Business forecasting John E. Hanke ; Arthur G. Reitsch ; Dean W. Wichern |
| title_short | Business forecasting |
| title_sort | business forecasting |
| topic | Bedrijfsleven gtt Prognoses gtt Pronóstico de los negocios Prévision commerciale Tijdreeksen gtt Business forecasting Statistik (DE-588)4056995-0 gnd Prognose (DE-588)4047390-9 gnd Management (DE-588)4037278-9 gnd Prognoseverfahren (DE-588)4358095-6 gnd |
| topic_facet | Bedrijfsleven Prognoses Pronóstico de los negocios Prévision commerciale Tijdreeksen Business forecasting Statistik Prognose Management Prognoseverfahren |
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