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OM 544 Business Forecasting Models

Put your company’s future in sight.

8 weeks

If you could see the future, what business decisions would you make today? How would you better poise your company to succeed in the years to come? Of course, a crystal ball that can tell the future is a myth, but there are techniques and methods that can be used to better predict events in the economic/business environment that affect planning and decision making.

Your Flex Fit

Where does this course fall in your Flex curriculum?
OM 544 is one of five advanced courses offered in this program’s Operations Management (OM) specialization.

Course Overview

How accurately can you predict what may happen in the future? Forecasting is based on the data that are available now. The more you know about the past and the present, the more accurately you can foresee what the future holds.

That is the basis of quantitative business forecasting, which relies on a number of methods to interpret and analyze information such as

  • Decomposition
  • Averaging
  • Smoothing
  • Simple regression
  • Multiple regression
  • ARIMA modeling.

In OM 544, you’ll focus on the practical application of these methods in various business-forecasting situations. You’ll examine issues that are important in the selection of appropriate forecasting methodology.

  • Data requirements
  • Forecast accuracy
  • Time horizon and cost
  • Judgmental forecasting
  • Managing the forecasting process
Software Preview

You’ll gain experience using MINITAB statistical software to analyze data sets, fit forecasting models, prepare forecasts, and interpret results.

You’ll use simple linear regression as a springboard to delve into explanatory (causal) models for forecasting.

  • Using ordinary least squares (OLS) to estimate the regression line.
  • Recognizing the link between simple linear regression and correlation.
  • Testing the significance of model parameters.
  • Assessing model adequacy and goodness of the fitted model.

You’ll also examine topics involved in multiple regression analysis, a method that involves building models that relate two or more independent variables to the forecast variable.

  • Interpreting regression coefficients
  • Inference for multiple regression models
  • Stepwise regression
  • Best subsets regression
  • Dummy variables
  • The Durbin-Watson statistic

You’ll examine the role judgment plays in forecasting, including the types of possible biases; the potential benefits of "adjusting" forecasts from empirical/statistical models; and combining forecasts from different methods to obtain a "better" forecast.

Given the use of statistical software and other predictive methods, how big a role does judgment play in business forecasting?

Explore this and more in OM 544.

Sample Course Topics

  • Exploring Data Patterns and Forecast Accuracy
  • Decomposition Methods
  • Averaging and Smoothing Methods
  • Simple Linear Regression
  • Multiple Regression Analysis
  • ARIMA Models
  • Judgmental Forecasting and Managerial Issues

What You’ll Learn

In OM 544, you’ll learn the techniques and models associated with forecasting through data analysis

  • Understand the role of forecasting in effective managerial decision making.
  • Recognize patterns and identify dominant component(s) in a time series.
  • Apply time series and causal models to forecast business variables.
  • Use MINITAB statistical software to fit and evaluate various models and use them to forecast.
  • Discuss the issues relevant to selecting the best forecasting method for a given situation.
  • Explain the importance of judgment in forecasting.
  • Describe strategies for effectively managing the forecasting process within organizations.

Learn More

A variety of business forecasting methods can be applied to help managers make decisions based on predictive data. For more information about The University of Scranton’s online Master of Business Administration degree, request more information or call us today toll-free at (866) 373-9547.

The content presented on this page is representative information for example purposes and is subject to change as course and student needs change over time.