Forecasting - Advanced

Dr. Dean Wichern

Aim of Course:

Participants will learn to analyze time series data and develop forecasts of future values using readily available forecasting techniques. This course goes into more detail and depth than "Forecasting Time Series" and covers averaging methods, including exponential smoothing, regression models for time series and ARIMA (Box-Jenkins) models. The forecasting techniques discussed in this course can be implemented using most statistical software packages.

Who Should Take This Course:

This course is oriented toward practitioners and makes extensive use of real data based cases. Participants should be familiar with basic statistical concepts and, ideally, should have some forecasting experience. Business analysts in industry, government, service organizations and others who need to make future predictions from historical data should benefit from this course.

For those enrolled in a Program of Advanced Statistical Studies, this is a required or elective course in the following Programs:

• Statistics in Business & Marketing - elective

Course Program:

The course is structured as follows:

The Instructor:

Dr. Dean Wichern is Professor Emeritus of Business Statistics, Texas A&M University. He is the coauthor of Business Forecasting and Applied Multivariate Statistical Analysis, and articles in the Journal of the American Statistical Association, Management Science, Journal of Finance, Biometrika, Technometrics and more.

Organization of the Course:

The course takes place over the internet, at statistics.com. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor. The course is scheduled to take place over 4 weeks, and typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials and work through exercises. Discussion among participants is encouraged. The instructor will provide answers and comments.

Certificates and Grades:

You may be interested only in learning the material presented, and not be concerned with grades or certificates. Or you may be enrolled in a statistics.com Program in Advanced Statistical Studies that requires demonstration of proficiency in the subject, in which case your work will be assessed for purposes of issuing a grade. Or you may require only a "Certificate of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). As you begin the class, you will be asked to specify your category.

Credit:

This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a certificate will be issued by statistics.com, upon request.

Dates:

Oct. 22 - Nov. 19, 2010
Click here to be notified of future course offerings.

Participants gain access to the online materials on the first day of the course, and typically spend about 15 hours per week (at their convenience). You retain full access to course materials, including discussion board, for two weeks after the course closing date.

Level:

intermediate

Prerequisite:

The equivalent of Introduction to Statistics 1: Inference for a Single Variable, and Introduction to Statistics 2: Working with Bivariate Data (and, if necessary before these courses, Introduction to Statistics for Beginners or Survey of Statistics for Beginners).

For additional information about course prerequisites, click here. Students should also have some familiarity with basic time series forecasting, such as that covered in Forecasting Time Series 1.

Course Text:

The required text for this course is Hanke and Wichern's 9th Edition of Business Forecasting and Student CD Package,which can be ordered directly from the publisher here. Addison Wesley/Pearson offers a 15% discount on this book (and all other statistics titles): enter the code STATSMPS in the Promotion Code field when prompted during checkout and click the Apply Discount button. The 15% off discount will work for customers coming from anywhere in the world, except Canada. The free shipping only applies to orders shipping within the US.

Special note for Canadian customers: If you are located in Canada, you will need to make your order on the myPearsonStore.ca site, which does not have the ability to offer discount codes. Try calling your regional Pearson representative. The US website will not let you choose "Canada" as a ship-to country.

PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

Note: This text is also used in "Forecasting Time Series 1."

Software:

Participants will benefit from having access to software that can do time series analysis. Examples and illustrations will be provided for Minitab, and the homework is designed so that it can all be done in Minitab. (Most standard statistical software will be able to handle the majority of the procedures covered in this course.) Our teaching assistants can offer help with Minitab and XLMiner. For information about software that is available at no charge, or nominal charge, for statistics.com courses click here.

Registration:

Register Online - $499
Register Online (academic) - $399 (you must be affiliated with a college, university or high school)

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Note: Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

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