Shane T. Jensen

All Courses

• GCB9950 - Dissertation

• STAT1020 - Intro Business Stat

  Continuation of STAT 1010 or STAT 1018. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications. This course may be taken concurrently with the prerequisite with instructor permission.

• STAT1110 - Introductory Statistics

  Introduction to concepts in probability. Basic statistical inference procedures of estimation, confidence intervals and hypothesis testing directed towards applications in science and medicine. The use of the JMP statistical package. Knowledge of high school algebra is required for this course.

• STAT3990 - Independent Study

  Written permission of instructor and the department course coordinator required to enroll in this course.

• STAT4420 - Intro Bayes Data Analys

  The course will introduce data analysis from the Bayesian perspective to undergraduate students. We will cover important concepts in Bayesian probability modeling as well as estimation using both optimization and simulation-based strategies. Key topics covered in the course include hierarchical models, mixture models, hidden Markov models and Markov Chain Monte Carlo. A course in probability (STAT 4300 or equivalent); a course in statistical inference (STAT 1020, STAT 1120, STAT 4310 or equivalent); and experience with the statistical software R (at the level of STAT 4050 or STAT 4700) are recommended.

• STAT5420 - Bayesian Meth & Comp

  Sophisticated tools for probability modeling and data analysis from the Bayesian perspective. Hierarchical models, mixture models and Monte Carlo simulation techniques.

• STAT6130 - 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.

• STAT8990 - Independent Study

  Written permission of instructor, the department MBA advisor and course coordinator required to enroll.

• STAT9270 - Bayesian Statistics

  This graduate course will cover the modeling and computation required to perform advanced data analysis from the Bayesian perspective. We will cover fundamental topics in Bayesian probability modeling and implementation, including recent advances in both optimization and simulation-based estimation strategies. Key topics covered in the course include hierarchical and mixture models, Markov Chain Monte Carlo, hidden Markov and dynamic linear models, tree models, Gaussian processes and nonparametric Bayesian strategies.

• STAT9950 - Dissertation

• STAT9990 - Independent Study

  Written permission of instructor and the department course coordinator required to enroll.

• Wharton Undergraduate Teaching Excellence Award, 2021
• Wharton Teaching Excellence Award, 2020
• Wharton Teaching Excellence Award, 2019
• SABR Analytics Conference Research Award in Contemporary Baseball Analysis, 2016 Description

  for the paper “OpenWAR: an open source system for evaluating overall player performance in major league baseball.”

• Sports in Statistics Award for Contributions to the Statistics in Sports Community, American Statistical Association, 2011
• David W. Hauck Award for Excellence in Undergraduate Teaching, The Wharton School, 2009
• Leonard J. Savage Award for best thesis in Application Methodology from the International Society for Bayesian Analysis, 2005

Knowledge at Wharton

• How Urban Planners Can Encourage ‘Vibrancy’ — and Create Safer Cities, Knowledge at Wharton - 6/12/2017
• Sports by the Numbers: Predicting Winners and Losers, Knowledge at Wharton - 4/20/2012
• The Use — and Misuse — of Statistics: How and Why Numbers Are So Easily Manipulated, Knowledge at Wharton - 4/2/2008

Wharton Stories

• How to Use Big Data to Make Cities Safer, Wharton Stories - 04/09/2018