Business forecasting:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Upper Saddle River, NJ
Prentice-Hall
1998
|
| Ausgabe: | 6. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVII, 581 S. graph. Darst. |
| ISBN: | 0137607458 |
Internformat
MARC
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| 245 | 1 | 0 | |a Business forecasting |c John E. Hanke ; Arthur G. Reitsch |
| 250 | |a 6. ed. | ||
| 264 | 1 | |a Upper Saddle River, NJ |b Prentice-Hall |c 1998 | |
| 300 | |a XVII, 581 S. |b graph. Darst. | ||
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Datensatz im Suchindex
| _version_ | 1804127283770818560 |
|---|---|
| adam_text | Contents
Preface xv
Acknowledgments xvii
1 INTRODUCTION TO FORECASTING 1
History of Forecasting 1
Need for Forecasting 2
Types of Forecasts 4
Macroeconomic Forecasting 4
Choosing a Forecasting Method 5
Forecasting Steps 6
Managing the Forecasting Process 7
Computer Forecasting Packages 8
Summary 9
Case Study 1.1: Mr. Tux 10
Case Study 1.2: Consumer Credit Counseling 10
Introduction to the Minitab Computer Package 11
Forecasting Using Spreadsheets 12
Appendix—Dedicated Forecasting Software 13
Selected Bibliography 17
2 A REVIEW OF BASIC STATISTICAL CONCEPTS 18
Descriptive Statistics 18
Probability Distributions 21
Sampling Distributions 26
vii
viii Contents
Estimation 28
Hypothesis Testing 29
Goodness of Fit Test 34
Correlation Analysis 36
Scatter Diagrams 37
Correlation Coefficient 40
Application to Management 44
Glossary 45
Key Formulas 46
Problems 47
Case Study 2.1: Alcam Electronics 52
Case Study 2.2: Mr. Tux 55
Minitab Computer Package 56
Forecasting Using Spreadsheets 59
Selected Bibliography 61
3 DATA SOURCES 63
Introduction 63
Data Types 64
Data Sources 65
Secondary Data Sources 66
External Sources 66
Private Data 66
Primary Data Source 69
Application to Management 75
Glossary 77
Problems 77
Case Study 3.1: Argyl Food Products 78
Case Study 3.2: Solomon s Jewelry 81
Case Study 3.3: Mr. Tux 82
Case Study 3.4: Consumer Credit Counseling 83
Minitab Computer Package 84
Forecasting Using Spreadsheets 86
Selected Bibliography 87
4 EXPLORING DATA PATTERNS AND CHOOSING
A FORECASTING TECHNIQUE 88
Time Series Components 89
Exploring Data Patterns with Autocorrelation Analysis 91
Choosing a Forecasting Technique 106
Forecasting Techniques for Stationary Data 107
Contents jx
Forecasting Techniques for Data with a Trend 108
Forecasting Techniques for Data with Seasonality 108
Forecasting Techniques for Cyclical Series 109
Other Factors to Consider When Choosing a Forecasting
Technique 110
Measuring Forecasting Error 111
Determining the Adequacy of a Forecasting Technique 115
Application to Management 116
Glossary 118
Key Formulas 118
Problems 119
Case Study 4.1: Murphy Brothers Furniture 124
Case Study 4.2: Mr. Tux 127
Case Study 4.3: Consumer Credit Counseling 129
Minitab Computer Package 130
Forecasting Using Spreadsheets 132
Bibliography 135
5 MOVING AVERAGES AND SMOOOTHING METHODS 136
Naive Models 137
Averaging Methods 141
Simple Averages 141
Moving Averages 142
Double Moving Average 145
Exponential Smoothing Methods 148
Tracking 154
Double Exponential Smoothing 156
Exponential Smoothing Adjusted for Trend: Holt s Method 162
Exponential Smoothing Adjusted for Trend and Seasonal Variation:
Winter s Model 165
Application to Management 170
Glossary 171
Key Formulas 171
Problems 172
Case Study 5.1: The Solar Alternative Company 177
Case Study 5.2: Mr. Tux 179
Case Study 5.3: Consumer Credit Counseling 180
Case Study 5.4: Five Year Revenue Projection for
Downtown Radiology 180
Minitab Computer Package 189
Forecasting Using Spreadsheets 190
Selected Bibliography 192
x Contents
6 REGRESSION ANALYSIS 193
Regression Line 195
Standard Error of Estimate 198
Predicting Y 199 j
Coefficient of Determination 203
Residuals 206
Hypothesis Testing 210
Computer Output 213
Variable Transformations 216
Application to Management 218
Glossary 223
Key Formulas 223 j
Problems 224
Case Study 6.1: Tiger Transport 231
Case Study 6.2: Butcher Products, Inc. 234
Case Study 6.3: Ace Personnel Department 236
Case Study 6.4: Mr. Tux 237
Case Study 6.5: Consumer Credit Counseling 238
Minitab Computer Package 239
Forecasting Using Spreadsheets 241
Selected Bibliography 242
7 MULTIPLE REGRESSION 243
Predictor Variables 243
Correlation Matrix 244
Multiple Regression Equation 245
Regression Coefficients 248
Statistical Inference in Multiple Regression 249
Residuals 250
Standard Error of Estimate 252
Computer Output 252
Dummy Variables 254
Validation of the Model 258
Heteroscedasticity 259
Collinearity 259
Selecting the Best Regression Equation 262
All Possible Regressions 265
Stepwise Regression 267
Final Notes on Stepwise Regression 272
Using Regression to Forecast Seasonal Data 272
Econometric Forecasting 275
Overfitting 277
Application to Management 277
Contents xi
Glossary 279
Key Formulas 279
Problems 280
Case Study 7.1: The Bond Market 287
Case Study 7.2: Restaurant Sales 290
Case Study 7.3: Mr. Tux 293
Case Study 7.4: Consumer Credit Counseling 295
Minitab Computer Package 297
Forecasting Using Spreadsheets 298
Selected Bibliography 301
8 TIME SERIES ANALYSIS 302
Decomposition 303
Price Index 304
Trend 307
Trend Curves 310
Cyclical Variation 313
Long Term Forecasting 320
Seasonal Variation 321
Seasonally Adjusted Data 325
Short Term Components 326
Seasonal Trend 326
Cyclical and Irregular Variations 327
Seasonal Forecasting 332
The Census II Decomposition Method 333
Application of Management 334
Glossary 335
Key Formulas 336
Problems 336
Case Study 8.1: The Small Engine Doctor 341
Case Study 8.2: Mr. Tux 343
Case Study 8.3: Consumer Credit Counseling 347
Case Study 8.4: AAA Washington 348
Minitab Computer Package 350
Forecasting Using Spreadsheets 353
Selected Bibliography 356
9 REGRESSION OF TIME SERIES DATA 357
The Problem of Heteroscedasticity When Regressing
Time Series Data 357
The Problem of Serial Correlation When Regressing
Time Series Data 358
Durbin Watson Test for Serial Correlation 362
xii Contents
Solutions to Serial Correlation Problems 365
Model Specification Error (Omitting a Variable) 366
Regression of Percentage Changes 369
Autoregressive Models 374
Generalized Least Squares 374
First Differences 377
Iterative Approach 377
Application to Management 380
Glossary 381
Key Formulas 381
Problems 382
Case Study 9.1: Company of Your Choice 388
Case Study 9.2: Business Activity Index for
Spokane County 389
Case Study 9.3: Mr. Tux 395
Case Study 9.4: Consumer Credit Counseling 396
Case Study 9.5: AAA Washington 397
Minitab Computer Package 400
Forecasting Using Spreadsheets 401
TSP Example 403
Selected Bibliography 406
10 THE BOX JENKINS (ARIMA) METHODOLOGY 407
Box Jenkins Technique 407
Partial Autocorrelations 412
Autoregressive Model 412
Moving Average Models 414
Autoregressive Moving Average Models 415
Applying the Methodology 416
Stage 1: Model Identification 416
Stage 2: Model Estimation and Testing of Model Adequacy 416
Stage 3: Forecasting with the Model 418
A Seasonal Analysis 441
Exponential Smoothing 452
Advantages and Disadvantages 453
Application to Management 454
Glossary 456
Key Formulas 456
Problems 457
Case Study 10.1: Restaurant Sales 468
Case Study 10.2: Mr. Tux 469
Case Study 10.3: Consumer Credit Counseling 471
Case Study 10.4: The Lydia E. Pinkham Medicine Company 472
Contents xiii
Case Study 10.5: Daily Demand for Trout Tags 475
Minitab Computer Package 488
Forecasting Using Spreadsheets 490
Selected Bibliography 493
11 JUDGMENTAL ELEMENTS IN FORECASTING 494
Judgmental Forecasting 494
Growth Curves 496
The Delphi Method 497
Scenario Writing 497
Combining Forecasts 498
Forecasting and Neural Networks 499
Summary of Judgmental Forecasting 501
Other Judgmental Elements in Forecasting 502
Managing the Forecasting Process 507
The Forecasting Process 508
Monitoring Forecasts 509
Forecasting Steps Reviewed 514
Forecasting Responsibility 515
Forecasting Costs 516
Selling Management on Forecasting 516
Forecasting and the MIS System 517
The Future of Forecasting 517
Glossary 518
Key Formula 518
Case Study 11.1: Boundary Electronics 518
Case Study 11.2: Golden Gardens Restaurant 519
Case Study 11.3: Busby Associates 520
Case Study 11.4: Mr. Tux 525
Case Study 11.5: Consumer Credit Counseling 527
Case Study 11.6: The Lydia E. Pinkham Medicine
Company Revisited 527
Selected Bibliography 531
APPENDIXES 532
A DERIVATIONS 533
B DATA FOR CASE STUDY 7.1 535
C TABLES 538
D DATA SETS AND DATABASE 551
INDEX 575
|
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| discipline | Wirtschaftswissenschaften |
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| spelling | Hanke, John E. Verfasser aut Business forecasting John E. Hanke ; Arthur G. Reitsch 6. ed. Upper Saddle River, NJ Prentice-Hall 1998 XVII, 581 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Administracao de empresas larpcal Business forecasting Prognose (DE-588)4047390-9 gnd rswk-swf Management (DE-588)4037278-9 gnd rswk-swf Prognoseverfahren (DE-588)4358095-6 gnd rswk-swf Statistik (DE-588)4056995-0 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 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008578820&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. Business forecasting Administracao de empresas larpcal Business forecasting Prognose (DE-588)4047390-9 gnd Management (DE-588)4037278-9 gnd Prognoseverfahren (DE-588)4358095-6 gnd Statistik (DE-588)4056995-0 gnd |
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| title | Business forecasting |
| title_auth | Business forecasting |
| title_exact_search | Business forecasting |
| title_full | Business forecasting John E. Hanke ; Arthur G. Reitsch |
| title_fullStr | Business forecasting John E. Hanke ; Arthur G. Reitsch |
| title_full_unstemmed | Business forecasting John E. Hanke ; Arthur G. Reitsch |
| title_short | Business forecasting |
| title_sort | business forecasting |
| topic | Administracao de empresas larpcal Business forecasting Prognose (DE-588)4047390-9 gnd Management (DE-588)4037278-9 gnd Prognoseverfahren (DE-588)4358095-6 gnd Statistik (DE-588)4056995-0 gnd |
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