Business forecasting 9th Edition by John E Hanke, Dean W Wichern ISBN 9780132301206 by Hanke, John E;wichern, Dean 9781292023007, 9781292036182, 1292023007, 1292036184 instant download after payment.

Business forecasting 9th Edition by John E Hanke, Dean W Wichern - Ebook PDF Instant Download/Delivery: 9780132301206
Full download Business forecasting 9th Edition after payment

Product details:

ISBN 13: 9780132301206
Author: John E Hanke, Dean W Wichern

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. For undergraduate and graduate courses in Business Forecasting.   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.

Business forecasting 9th Table of contents:

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

People also search for Business forecasting 9th:

business forecasting principles
    
practical business forecasting
    
purpose of business forecasting
    
predictive analytics for business forecasting and planning pdf
    
principles of business forecasting 2nd ed pdf
    
predictive analytics for business forecasting

Tags: John E Hanke, Dean W Wichern, Business, forecasting