Business Forecasting, 9e John Hanke, Dean Wichern (Solution Manual)

CHAPTER 1 Introduction to Forecasting The History of Forecasting Is Forecasting Necessary? Types of Forecasts Macroeconomic Forecasting Considerations Choosing a Forecasting Method Forecasting Steps Managing the F ...

CHAPTER 1 Introduction to Forecasting The History of Forecasting Is Forecasting Necessary? Types of Forecasts Macroeconomic Forecasting Considerations Choosing a Forecasting Method Forecasting Steps Managing the Forecasting Process Forecasting Software Online Information Forecasting Examples Summary Case 1-1: Mr. Tux Case 1-2: Consumer Credit Counseling Minitab Applications Excel Applications References CHAPTER 2 A Review of Basic Statistical Concepts Describing Data with Numerical Summaries Displays of Numerical Information Probability Distributions Sampling Distributions Inference from a Sample Estimation Hypothesis Testing p-Value Correlation Analysis Scatter Diagrams Correlation Coefficient Fitting a Straight Line Assessing Normality Application to Management Glossary Key Formulas Problems Case 2-1: Alcam Electronics Case 2-2: Mr. Tux Case 2-3: Alomega Food Stores Minitab Applications Excel Applications References CHAPTER 3 Exploring Data Patterns and an Introduction to Forecasting Techniques Exploring Time Series Data Patterns Exploring Data Patterns with Autocorrelation Analysis Are the Data Random? Do the Data Have a Trend? Are the Data Seasonal? Choosing a Forecasting Technique Forecasting Techniques for Stationary Data Forecasting Techniques for Data with a Trend Forecasting Techniques for Seasonal Data Forecasting Techniques for Cyclical Series Other Factors to Consider When Choosing a Forecasting Technique Empirical Evaluation of Forecasting Methods Measuring Forecast Error Determining the Adequacy of a Forecasting Technique Application to Management Glossary Key Formulas Problems Case 3-1A: Murphy Brothers Furniture Case 3-1B: Murphy Brothers Furniture Case 3-2: Mr. Tux Case 3-3: Consumer Credit Counseling Case 3-4: Alomega Food Stores Case 3-5: Surtido Cookies Minitab Applications Excel Applications References CHAPTER 4 Moving Averages and Smoothing Methods Naive Models Forecasting Methods Based on Averaging Simple Averages Moving Averages Double Moving Averages Exponential Smoothing Methods Exponential Smoothing Adjusted for Trend: Holt’s Method Exponential Smoothing Adjusted for Trend and Seasonal Variation:Winter’s Method Application to Management Glossary Key Formulas Problems Case 4-1: The Solar Alternative Company Case 4-2: Mr. Tux Case 4-3: Consumer Credit Counseling Case 4-4: Murphy Brothers Furniture Case 4-5: Five-Year Revenue Projection for Downtown Radiology Case 4-6: Web Retailer Case 4-7: Southwest Medical Center Case 4-8: Surtido Cookies Minitab Applications Excel Applications References CHAPTER 5 Time Series and Their Components Decomposition Trend Additional Trend Curves Forecasting Trend Seasonality Seasonally Adjusted Data Cyclical and Irregular Variations Summary Example Business Indicators Forecasting a Seasonal Time Series The Census II Decomposition Method Application to Management Appendix: Price Index Glossary Key Formulas Problems Case 5-1: The Small Engine Doctor Case 5-2: Mr. Tux Case 5-3: Consumer Credit Counseling Case 5-4: Murphy Brothers Furniture Case 5-5: AAA Washington Case 5-6: Alomega Food Stores Case 5-7: Surtido Cookies Case 5-8: Southwest Medical Center Minitab Applications Excel Applications References CHAPTER 6 Simple Linear Regression Regression Line Standard Error of the Estimate Forecasting Y Decomposition of Variance Coefficient of Determination Hypothesis Testing Analysis of Residuals Computer Output Variable Transformations Growth Curves Application to Management Glossary Key Formulas Problems Case 6-1: Tiger Transport Case 6-2: Butcher Products, Inc. Case 6-3: Ace Manufacturing Case 6-4: Mr. Tux Case 6-5: Consumer Credit Counseling Case 6-6: AAA Washington Minitab Applications Excel Applications References CHAPTER 7 Multiple Regression Analysis Several Predictor Variables Correlation Matrix Multiple Regression Model Statistical Model for Multiple Regression Interpreting Regression Coefficients Inference for Multiple Regression Models Standard Error of the Estimate Significance of the Regression Individual Predictor Variables Forecast of a Future Response Computer Output Dummy Variables Multicollinearity Selecting the “Best” Regression Equation All Possible Regressions Stepwise Regression Final Notes on Stepwise Regression Regression Diagnostics and Residual Analysis Forecasting Caveats Overfitting Useful Regression, Large F Ratios Application to Management Glossary Key Formulas Problems Case 7-1: The Bond Market Case 7-2: AAA Washington Case 7-3: Fantasy Baseball (A) Case 7-4: Fantasy Baseball (B) Minitab Applications Excel Applications References CHAPTER 8 Regression with Time Series Data Time Series Data and the Problem of Autocorrelation Autocorrelation and the Durbin-Watson Test Solutions to Autocorrelation Problems Model Specification Error (Omitting a Variable) Regression with Differences Autocorrelated Errors and Generalized Differences Autoregressive Models Summary Time Series Data and the Problem of Heteroscedasticity Using Regression to Forecast Seasonal Data Econometric Forecasting Cointegrated Time Series Application to Management Glossary Key Formulas Problems Case 8-1: Company of Your Choice Case 8-2: Business Activity Index for Spokane County Case 8-3: Restaurant Sales Case 8-4: Mr. Tux Case 8-5: Consumer Credit Counseling Case 8-6: AAA Washington Case 8-7: Alomega Food Stores Case 8-8: Surtido Cookies Case 8-9: Southwest Medical Center Minitab Applications Excel Applications References CHAPTER 9 The Box-Jenkins (ARIMA) Methodology Box-Jenkins Methodology Autoregressive Models Moving Average Models Autoregressive Moving Average Models Summary Implementing the Model-Building Strategy Step 1: Model Identification Step 2: Model Estimation Step 3: Model Checking Step 4: Forecasting with the Model Model-Building Caveats Model Selection Criteria ARIMA Models for Seasonal Data Simple Exponential Smoothing and an ARIMA Model Advantages and Disadvantages of ARIMA Models Application to Management Glossary Key Formulas Problems Case 9-1: Restaurant Sales Case 9-2: Mr. Tux Case 9-3: Consumer Credit Counseling Case 9-4: The Lydia E. Pinkham Medicine Company Case 9-5: City of College Station Case 9-6: UPS Air Finance Division Case 9-7: AAA Washington Case 9-8: Web Retailer Case 9-9: Surtido Cookies Case 9-10: Southwest Medical Center Minitab Applications References CHAPTER 10 Judgmental Forecasting and Forecast Adjustments Judgmental Forecasting The Delphi Method Scenario Writing Combining Forecasts Forecasting and Neural Networks Summary of Judgmental Forecasting Other Tools Useful in Making Judgments About the Future Key Formulas Problems Case 10-1: Golden Gardens Restaurant Case 10-2: Alomega Food Stores Case 10-3: The Lydia E. Pinkham Medicine Company References CHAPTER 11 Managing the Forecasting Process The Forecasting Process Monitoring Forecasts Forecasting Steps Reviewed Forecasting Responsibility Forecasting Costs Forecasting and Management Information Systems Selling Management on Forecasting The Future of Forecasting Problems Case 11-1: Boundary Electronics Case 11-2: Busby Associates Case 11-3: Consumer Credit Counseling Case 11-4: Mr. Tux Case 11-5: Alomega Food Stores Case 11-6: Southwest Medical Center