Robert A. Stine

Current Courses (Fall 2024)

• STAT5350 - Forecasting Methods Mgmt

  This course provides an introduction to the wide range of techniques available for statistical modelling and forecasting of time series. Regression methods for decomposition models, trends and seasonality, spectral analysis, distributed lag models, autoregressive-moving average modeling, forecasting, exponential smoothing, and ARCH and GARCH models will be surveyed. The emphasis will be on applications, rather than technical foundations and derivations. The techniques will be studied critically, with examination of their usefulness and limitations.

  STAT5350401 ( Syllabus )

• STAT7110 - Forecasting Methods Mgmt

  This course provides an introduction to the wide range of techniques available for statistical modelling and forecasting of time series. Regression methods for decomposition models, trends and seasonality, spectral analysis, distributed lag models, autoregressive-moving average modeling, forecasting, exponential smoothing, and ARCH and GARCH models will be surveyed. The emphasis will be on applications, rather than technical foundations and derivations. The techniques will be studied critically, with examination of their usefulness and limitations. This course may be taken concurrently with the prerequisite with instructor permission.

  STAT7110401 ( Syllabus )

All Courses

• 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.

• STAT3990 - Independent Study

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

• STAT4050 - Stat Computing with R

  The goal of this course is to introduce students to the R programming language and related eco-system. This course will provide a skill-set that is in demand in both the research and business environments. In addition, R is a platform that is used and required in other advanced classes taught at Wharton, so that this class will prepare students for these higher level classes and electives.

• STAT4220 - Predictive Analytics

  This course follows from the introductory regression classes, STAT 1020, STAT 1120, and STAT 4310 for undergraduates and STAT 6130 for MBAs. It extends the ideas from regression modeling, focusing on the core business task of predictive analytics as applied to realistic business related data sets. In particular it introduces automated model selection tools, such as stepwise regression and various current model selection criteria such as AIC and BIC. It delves into classification methodologies such as logistic regression. It also introduces classification and regression trees (CART) and the popular predictive methodologies known as random forest and boosted trees. By the end of the course the student will be familiar with and have applied these concepts and will be ready to use them in a work setting. The methodologies are implemented in a variety of software packages. Applications in JMP emphasize concepts and key modeling decisions. This course may be taken concurrently with the prerequisite with instructor permission.

• STAT5350 - Forecasting Methods Mgmt

  This course provides an introduction to the wide range of techniques available for statistical modelling and forecasting of time series. Regression methods for decomposition models, trends and seasonality, spectral analysis, distributed lag models, autoregressive-moving average modeling, forecasting, exponential smoothing, and ARCH and GARCH models will be surveyed. The emphasis will be on applications, rather than technical foundations and derivations. The techniques will be studied critically, with examination of their usefulness and limitations.

• STAT7050 - Stat Computing with R

  The goal of this course is to introduce students to the R programming language and related eco-system. This course will provide a skill-set that is in demand in both the research and business environments. In addition, R is a platform that is used and required in other advanced classes taught at Wharton, so that this class will prepare students for these higher level classes and electives.

• STAT7110 - Forecasting Methods Mgmt

  This course provides an introduction to the wide range of techniques available for statistical modelling and forecasting of time series. Regression methods for decomposition models, trends and seasonality, spectral analysis, distributed lag models, autoregressive-moving average modeling, forecasting, exponential smoothing, and ARCH and GARCH models will be surveyed. The emphasis will be on applications, rather than technical foundations and derivations. The techniques will be studied critically, with examination of their usefulness and limitations. This course may be taken concurrently with the prerequisite with instructor permission.

• STAT7220 - Predictive Analytics

  This course follows from the introductory regression classes, STAT 1020, STAT 1120, and STAT 4310 for undergraduates and STAT 6130 for MBAs. It extends the ideas from regression modeling, focusing on the core business task of predictive analytics as applied to realistic business related data sets. In particular it introduces automated model selection tools, such as stepwise regression and various current model selection criteria such as AIC and BIC. It delves into classification methodologies such as logistic regression. It also introduces classification and regression trees (CART) and the popular predictive methodologies known as random forest and boosted trees. By the end of the course the student will be familiar with and have applied these concepts and will be ready to use them in a work setting. The methodologies are implemented in a variety of software packages. Applications in JMP emphasize concepts and key modeling decisions. This course is formerly STAT 6220.

• STAT7240 - Text Analytics

  This course introduces modern text analytics, and the tools of natural language processing. Text and language are powerful repositories of knowledge and information, but the semi-structured nature of language makes deriving insights from text challenging. Modern analytic techniques introduced in this course make it significantly easier even for non-specialists to use text and language data to drive deep insights. The course will use several examples from real world applications in different industries such as ecommerce, healthcare and finance to illustrate these techniques. Students should be familiar with regression models at the level of Stat 6130 or Stat 1020, and the Python language at the level of Stat 4770 or Stat 7770. Familiarity with the Jupyter notebook development environment is presumed, as well as common Python packages such as pandas, NLTK and SpaCy. Those with more knowledge of Statistics, such as from Stat 7220/4220, or computing skills will benefit. The predominant software used in the course is Jupyter notebooks that use a Python interpreter. Familiarity with basic probability models is helpful but not presumed.

• STAT8990 - Independent Study

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

• STAT9950 - Dissertation

  Dissertation

• Wharton MBA Excellence in Teaching Award, 2012
• Helen Kardon Moss Anvil Award for MBA Teaching, 2011
• Wharton MBA Excellence in Teaching Award, 2009
• Wharton MBA Excellence in Teaching Award, 2006-2007
• Excellence in Teaching Award (Undergraduate Division), 2004
• Miller-Sherrerd Wharton MBA Core Teaching Award, 2003
• David W. Hauck Award for Outstanding Teaching, 2001
• Excellence in Teaching Award (Undergraduate Division), 2001
• Miller-Sherrerd Wharton MBA Core Teaching Award, 1999
• Wharton MBA Core Curriculum Teaching Award, 1996
• Miller-Sherrerd Wharton MBA Core Teaching Award, 1994-1995
• Excellence in Teaching Award (Undergraduate Division), 1995

Knowledge at Wharton

• More Savings, Less Plastic: Consumer Credit after the Crisis, Knowledge at Wharton - 7/8/2009
• Polling the Polling Experts: How Accurate and Useful Are Polls These Days?, Knowledge at Wharton - 11/14/2007
• Tip of the Spear: Leadership Lessons from the U.S.-led Armed Forces in the Middle East, Knowledge at Wharton - 5/17/2006
• Mining Data for Nuggets of Knowledge, Knowledge at Wharton - 12/10/1999

Wharton Stories

• How This PhD Student Discovered a Dynamic Research Community at Wharton, Wharton Stories - 11/13/2014