McCombs School of Business

Available dates

May 11—12, 2020
2 days
Austin, Texas, United States
USD 2950
USD 1475 per day

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About the course

Discover, analyze and forecast relationships among large data sets (“Big Data”).

Gain confidence in building reliable data analyses to make projections of business intelligence and performance. Utilize the fundamental analytical tool for discovering, analyzing and forecasting relationships—regression. Apply regression to past relationships, looking for trends, seasonal patterns and hidden correlations that can predict the future reliably. Model customer retention rates, develop an optimal bidding strategy in a sealed bid process, hedge your firm’s revenue, or forecast future profitability of individual customers, monthly sales, or daily stock prices by charting a successful course with regression and forecasting methods. Acquire a solid fundamental understanding of the methods, using intuitive graphical approaches to explain and motivate regression and forecasting models.

Business Analytics

Explore the ins and outs of data analytics to gain perspective and experience with the complexities of “Big Data” in the business and organizational context.

Two-day Concentrated Program

Registration begins at 8:00 a.m. and class runs 8:30 – 4:30 both days with a one-hour networking lunch included.

Austin, Texas

Enjoy proximity to the vibrancy of the campus and downtown Austin, and the innovative, business-friendly environment of the city.

Benefits

  • Apply regression 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
  • Analyze case studies to gain a thorough consideration of the models applications

Topics

  • Forecasting models
  • Random samples
  • Random walks
  • Autoregression
  • Moving averages
  • ARIMA (Autoregressive Integrated Moving Average)
  • Regression analytics
  • Regression case studies

Who should attend

  • Those with a small to moderate working knowledge of statistics
  • Those seeking a refresher of the tools and models in practical application

Trust the experts

Tom Shively

Biography Shively, Thomas S. Professor, Department of Information, Risk, and Operations Management. Tom Shively received his B.A. from Middlebury College and his M.B.A. and Ph.D. from the University of Chicago. His research and teaching interests include time series regression models, nonparamet...

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Thomas Sager

Biography Sager, Thomas W. Professor, Department of Information, Risk, and Operations Management Tom Sager received his B.A., M.S., and his Ph.D. from the University of Iowa. His teaching and research interests are in applied and theoretical statistics, insurance and financial statistics.

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