Edward I. George

Current Courses

• STAT621 - Acc Regression Analysis

  STAT 621 is intended for students with recent, practical knowledge of the use of regression analysis in the context of business applications. This course covers the material of STAT 613, but omits the foundations to focus on regression modeling. The course reviews statistical hypothesis testing and confidence intervals for the sake of standardizing terminology and introducing software, and then moves into regression modeling. The pace presumes recent exposure to both the theory and practice of regression and will not be accommodating to students who have not seen or used these methods previously. The interpretation of regression models within the context of applications will be stressed, presuming knowledge of the underlying assumptions and derivations. The scope of regression modeling that is covered includes multiple regression analysis with categorical effects, regression diagnostic procedures, interactions, and time series structure. The presentation of the course relies on computer software that will be introduced in the initial lectures.

  STAT621001 ( Syllabus )

  STAT621003 ( Syllabus )

  STAT621005 ( Syllabus )

Past Courses

• BSTA622 - STATISTICAL INFERENCE II

  This This class will cover the fundamental concepts of statistical inference. Topics include sufficiency, consistency, finding and evaluating point estimators, finding and evaluating interval estimators, hypothesis testing, and asymptotic evaluations for point and interval estimation.

• STAT399 - INDEPENDENT STUDY

• STAT613 - REGR ANALYSIS FOR BUS

  This course provides the fundamental methods of statistical analysis, the art and science if extracting information from data. The course will begin with a focus on the basic elements of exploratory data analysis, probability theory and statistical inference. With this as a foundation, it will proceed to explore the use of the key statistical methodology known as regression analysis for solving business problems, such as the prediction of future sales and the response of the market to price changes. The use of regression diagnostics and various graphical displays supplement the basic numerical summaries and provides insight into the validity of the models. Specific important topics covered include least squares estimation, residuals and outliers, tests and confidence intervals, correlation and autocorrelation, collinearity, and randomization. The presentation relies upon computer software for most of the needed calculations, and the resulting style focuses on construction of models, interpretation of results, and critical evaluation of assumptions.

• STAT621 - ACC REGRESSION ANALYSIS

  STAT 621 is intended for students with recent, practical knowledge of the use of regression analysis in the context of business applications. This course covers the material of STAT 613, but omits the foundations to focus on regression modeling. The course reviews statistical hypothesis testing and confidence intervals for the sake of standardizing terminology and introducing software, and then moves into regression modeling. The pace presumes recent exposure to both the theory and practice of regression and will not be accommodating to students who have not seen or used these methods previously. The interpretation of regression models within the context of applications will be stressed, presuming knowledge of the underlying assumptions and derivations. The scope of regression modeling that is covered includes multiple regression analysis with categorical effects, regression diagnostic procedures, interactions, and time series structure. The presentation of the course relies on computer software that will be introduced in the initial lectures.

• STAT995 - DISSERTATION

• STAT999 - INDEPENDENT STUDY

• Cornell University Distinguished Alumni for the Department of Statistical Sciences, 2018
• Simons Fellowship, Isaac Newton Institute for Mathematical Sciences, Cambridge, 2018
• Bohrer Lecturer, University of Illinois at Urbana-Champaign, 2017
• Fellow, International Society for Bayesian Analysis, 2014
• Challis Lecturer and Award for Outstanding Contributions to Statistics, University of Florida, 2012
• Geisser Distinguished Lecturer, University of Minnesota, 2012
• Palmetto Lecturer, University of South Carolina, 2012
• Loeb Lecturer, Washington University in St. Louis, 2011
• Medallion Lecturer, Institute of Mathematical Statistics, 2010
• Penn IUR Faculty Fellow, 2009-2010
• Hartley Memorial Lecturer, Texas A&M University, 2007
• Wharton Core Professor Award, “Tough But We’ll Thank in Five Years”, 2004
• Wharton Core Professor Award, “Goes Above and Beyond”, 2004
• ISI Highly Cited Researcher in Mathematics, 2004
• Excellence in Education Award, The University of Texas at Austin, 2001
• Faculty Honor Roll for Core Class Teaching, The University of Texas at Austin, 2000
• Lawrence Baxter Memorial Lecturer, SUNY at Stony Brook, 1998
• Dean’s Fellow, The University of Texas at Austin, 1998-1999
• CBA Foundation Award for Outstanding Research Contributions, The University of Texas at Austin, 1998
• Fellow, American Statistical Association, 1997
• Fellow, Center for Management of Operations and Logistics, The University of Texas at Austin, 1996
• Member, International Statistical Institute, 1996
• Joe D. Beasley Award for Teaching Excellence, The University of Texas at Austin, 1996
• Fellow, Institute of Mathematical Statistics, 1995
• CBA Foundation Award for Research Excellence, The University of Texas at Austin, 1995
• GBC Award for Excellence in Teaching of the Core Curriculum, Graduate School of Business, The University of Texas at Austin, 1993
• Spurgeon Bell Centennial Fellowship, Graduate School of Business, The University of Texas at Austin, 1993-1994
• McKinsey Award for Excellence in Teaching, The University of Chicago, 1987
• The Emory Williams Award for Excellence in Teaching, The University of Chicago, 1987

Knowledge @ Wharton

• ‘Every Time a Bell Rings …’, Knowledge @ Wharton - 04/08/2013