Use regression methods and statistical tools to better discover, analyze and forecast relationships among large data sets ("Big Data") to gain confidence in building reliable data analyses to make projections of business intelligence and performance.
The fundamental analytical tool for discovering, analyzing and forecasting relationships is regression. Forecasting applies regression to past relationships, looking for trends, seasonal patterns and hidden correlations that can be predicted reliably into the future. Whether it is modeling customer retention rates, developing an optimal bidding strategy in a sealed bid process, hedging a firm’s revenue, or forecasting future profitability of individual customers, monthly sales, or daily stock prices, managers can chart a successful course with regression and forecasting methods.
All of these and other case studies are covered in this class. In addition, the class conveys a solid fundamental understanding of the methods, using intuitive graphical approaches to explain and motivate regression and forecasting models.
- Forecasting models
- Random samples
- Random walks
- Moving averages
- ARIMA (Autoregressive Integrated Moving Average)
- Regression analytics
- Regression case studies
- Understand how regression can be used to uncover trends, patterns and data correlations
- Gain confidence when using data to make analyses, forecasts and projections
- Develop the acumen to competently evaluate the findings and analyses presented by others
- Interact with data executives on the topic of data-driven business intelligence
Tom Sager, Ph.D., Professor, Department of Information, Risk, and Operations Management
Tom Shively, Ph.D., Professor, Department of Information, Risk, and Operations Management