Introduction to Business Forecasting: Using Minitab |
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This web-based course is designed for working professionals and university students who want to enhance their understanding of business forecasting from an applied perspective. The course explains how forecasts are actually developed and utilized, emphasizing modern statistical methods and quantitative forecasting techniques. Specific applications to business include forecasting sales, production, inventory, macroeconomic factors such as interest rates and exchange rates, and other aspects of both short- and long-term business planning.
Course Specifications: |
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Length: |
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10 weeks |
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Weekly time commitment: |
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Approximately 5-10 hours |
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Prerequisites: |
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None, however familiarity with Microsoft Excel, basic algebra and basic statistics is preferred. |
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Tuition: |
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US $1,495 |
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Additional expenses: |
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Business Forecasting textbook, see below.
U.S. Edition is approximately $125. Foreign Edition prices vary by location. Minitab software must be purchased separately. |
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Course Materials - Text and Software
The course is based upon the text, Business Forecasting, by John Hanke and Dean Wichern. Published by Pearson-Prentice Hall, the text is in its 8th Edition. The text can be purchased directly from Pearson-Prentice Hall at www.prenhall.com/hanke or from online retailers such as Amazon.com. The ISBN is 0-13-141290-6.
A student version of Minitab is available at an attractive discounted rate at www.academicsuperstore.com. A full version of the software is available at www.minitab.com
System Requirements
The minimum system requirements necessary to complete the course are as follows:
- A 486X-based computer
- Windows™ 98, ME, NT 4, 2000, XP
- 300 MHz Processor
- 64 MB RAM
- CD ROM required for installation
- 85 MB hard disk space for full installation
Course Format
The course runs 10 weeks. Each week the student will read a chapter in the text and the associated lecture notes and complete assigned exercises and case studies using the Minitab software. A weekly quiz will enable the student to test his or her knowledge of the chapter material. An independent forecasting project will allow the student to apply the techniques they have learned to a real world situation of personal or professional interest.
Course Content
Unit 1: Introduction to Business Forecasting
The first unit introduces the student to the role of quantitative forecasting in business, and provides a brief historical background. Different types of forecasts are discussed and the forecast process is outlined. Popular forecasting software packages are identified and some online resources are noted. A forecasting example and two case studies – Mr. Tux and Consumer Credit Counseling – are provided. Minitab software is introduced.
Unit 2: A Review of Basic Statistical Concepts
Unit two provides a review of basic statistical concepts used in forecasting. Descriptive statistics and methods for displaying numerical information are discussed. Probability distributions and sampling distributions are explained in detail. Estimation and hypothesis testing are reviewed and a number of examples are provided. The relationship between variables is examined using the correlation coefficient regression analysis. Data analysis using Minitab and Excel is introduced.
Unit 3: Exploring Data Patterns and Choosing a Forecasting Technique
Data issues are discussed in this section including those related to the collection of valid and reliable data. Time series data patterns are explored. The autocorrelation function is examined. Issues involved with choosing a proper forecasting technique are introduced. Measuring forecast accuracy is outlined. Minitab and Excel applications are provided.
Unit 4: Moving Averages and Smoothing Methods
This unit introduces three simple approaches to forecasting a time series including naďve, averaging, and smoothing techniques. The strengths and weaknesses of each method are outlined and real-world examples provided. The appropriateness of methods based on the underlying data patterns is discussed. Management applications and case studies are provided. Minitab and Excel applications are illustrated.
Unit 5: Time Series and Their Components
Unit 5 introduces time series decomposition as a method of identifying the component factors of a data series. The trend, cyclical, seasonal, and irregular components of historical time series are examined. Numerous examples are provided. The use of seasonally adjusted data is illustrated. The Census II Decomposition Method is outlined. The calculation and use of indices is explained and demonstrated. Case studies and applications using Minitab and Excel are provided.
Unit 6: Simple Linear Regression
This unit introduces simple linear regression models and explains in detail how knowledge of an independent variable can be used to forecast a dependent variable. A procedure for fitting a regression line is outlined. The standard error of the estimate is introduced for measuring the extent to which the sample data points are spread around the fitted regression function. Using the regression line to forecast a value is discussed. The analysis of variance is explained in detail. The coefficient of determination is defined and its use explained. Hypothesis testing is revisited. Residuals are evaluated. Interpreting computer regression output data is detailed. Variable transformations are illustrated. Applications to management and case studies are provided. Regression analysis using Minitab and Excel is demonstrated.
Unit 7: Multiple Regression Analysis
Unit 7 extends the simple linear regression model introduced in the prior unit to include several predictor variables. The multiple regression model is defined and the use of the correlation matrix in specifying the model is demonstrated. Interpreting regression results is explained. Using the model to make inferences is illustrated. A test of the significance of the regression is outlined. Dummy variables are introduced as a means for measuring the influence of a qualitative factor in a regression model. The problem of multicollinearity is defined and explained in detail. Guidelines for selecting the "best" regression equation are provided. Some regression diagnostics are introduced. Numerous case studies and a solution illustrating the use of Minitab are provided.
Unit 8: Regression with Time Series Data
Unit 8 introduces various problems associated with regression on time series data. Specifically, the problems of autocorrelation and heteroscedasticity are addresses. The problems are explained in detail and methods for overcoming the problems are outlined. Econometric forecasting is introduced. Case studies and Minitab and Excel applications are presented.
Unit 9: The Box-Jenkins (ARIMA) Methodology
The Box-Jenkins (ARIMA) methodology is outlined in this unit. A procedure for identifying, fitting, and checking ARIMA models with time series data is presented and illustrated in detail. Autoregressive, moving average, and mixed models are defined. A model-building strategy is implemented. Minitab outputs for ARIMA models are evaluated. The advantages and disadvantages of ARIMA models are outlined. Problems, case studies and Minitab applications are presented.
Unit 10: Judgmental Forecasting and Managing the Forecasting
This unit discusses some important judgmental forecasting methods which may supplement the quantitative methods introduced in earlier units. The benefits derived from combining forecasts are discussed. The forecast process is outlined in detail including a summary of the strengths and weaknesses of the quantitative methods introduced in earlier units; a general step-by-step process for developing a forecast; and an overview of the key players and their roles in the forecast process.
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