Robert Stine

Education

PhD, Princeton University, 1982
MA, Princeton University, 1979
BS, University of South Carolina, 1977

Recent Consulting

Fraud detection in loan applications; validating models in use for consumer credit default.

Career and Recent Professional Awards

MillerSherrerd MBA Core Teaching Award, 2003, 2006, 2007, 2010
David W. Hauck Award for Outstanding Teaching, 2001
Excellence in Teaching Award (Undergraduate Division), 2001, 2004

Academic Positions Held

Wharton : 1979present (Research Associate, Analysis Center for Evaluation of Energy Modeling and Statistics, 197983, Director of Computing Analysis Center, 197983).

Previous appointments : Princeton University; University of Michigan; University of South Carolina. Visiting appointment: University of Michigan

Other Positions

Summer Intern, Office of Energy Information Administration, U.S. Department of Energy, 1978

For more information, go to My Personal Page

Robert A. Stine and Dean P. Foster, Statistics for Business: Decision Making and Analysis (2017)

Sivan AldorNoiman, Lawrence D. Brown, Emily Fox, Robert A. Stine (2016), Spatiotemporal low count processes with application to violent crime events, Statistica Sinica, to appear.

Kory Johnson, Dean P. Foster, Robert A. Stine (Working), Impartial Predictive Modeling: Ensuring Fairness in Arbitrary Models.

Dean P. Foster and Robert A. Stine (2015), Risk Inflation of Sequential Tests Controlled by Alpha Investing, Journal of Statistical Computation and Simulation, 85, pp. 36133627.

Robert A. Stine (Under Revision), Explaining normal quantile plots through animation: The waterfilling principle.

Dean P. Foster, J. Tetreaux, Robert A. Stine (Working), Grammatical error correction using streaming eigenword features.

Kory Johnson, Robert A. Stine, Dean P. Foster (Working), Submodularity in statistics: Comparing the success of model selection methods.

Dean P. Foster, Mark Liberman, Robert A. Stine (Working), Featurizing text: Converting text into predictors for regression analysis.

Kory Johnson, Dean P. Foster, Robert A. Stine (Working), Revisiting alpha investing: conditionally valid stepwise regression.

Dean P. Foster and Robert A. Stine (2014), Risk Inflation of Sequential Tests Controlled by Alpha Investing , Journal of Statistical Computation and Simulation.

Past Courses

STAT102 INTRO BUSINESS STAT

Continuation of STAT 101. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications.

STAT405 STAT COMPUTING WITH R

The goal of this course is to introduce students to the R programming language and related ecosystem. This course will provide a skillset 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.

STAT430 PROBABILITY

Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.

STAT510 PROBABILITY

Elements of matrix algebra. Discrete and continuous random variables and their distributions. Moments and moment generating functions. Joint distributions. Functions and transformations of random variables. Law of large numbers and the central limit theorem. Point estimation: sufficiency, maximum likelihood, minimum variance. Confidence intervals.

STAT511 STATISTICAL INFERENCE

Graphical displays; one and twosample confidence intervals; one and twosample hypothesis tests; one and twoway ANOVA; simple and multiple linear leastsquares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodnessoffit tests. A methodology course.

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.

STAT701 MODERN DATA MINING

Modern Data Mining: Statistics or Data Science has been evolving rapidly to keep up with the modern world. While classical multiple regression and logistic regression technique continue to be the major tools we go beyond to include methods built on top of linear models such as LASSO and Ridge regression. Contemporary methods such as KNN (K nearest neighbor), Random Forest, Support Vector Machines, Principal Component Analyses (PCA), the bootstrap and others are also covered. Text mining especially through PCA is another topic of the course. While learning all the techniques, we keep in mind that our goal is to tackle real problems. Not only do we go through a large collection of interesting, challenging reallife data sets but we also learn how to use the free, powerful software "R" in connection with each of the methods exposed in the class.

STAT705 STAT COMPUTING WITH R

The goal of this course is to introduce students to the R programming language and related ecosystem. This course will provide a skillset 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.

STAT712 DECISION MAKING

Fundamentals of modern decision analysis with emphasis on managerial decision making under uncertainty and risk. The basic topics of decision analysis are examined. These include payoffs and losses, utility and subjective probability, the value of information, Bayesian analysis, inference and decision making. Examples are presented to illustrate the ideas and methods. Some of these involve: choices among investment alternatives; marketing a new product; health care decisions; and costs, benefits, and sample size in surveys.

STAT910 FORCAS & TIME SER ANALY

Fourier analysis of data, stationary time series, properties of autoregressive moving average models and estimation of their parameters, spectral analysis, forecasting. Discussion of applications to problems in economics, engineering, physical science, and life science.

Helen Kardon Moss Anvil Award for MBA Teaching, 2011 Wharton MBA Core Teaching Award, 2010 MillerSherrerd MBA Core Teaching Award, 2006 Excellence in Teaching Award (Undergraduate Division), 2004 MillerSherrerd MBA Core Teaching Award, 2003 David W. Hauck Award for Outstanding Teaching, 2001 Excellence in Teaching Award (Undergraduate Division), 2001

Knowledge @ Wharton

More Savings, Less Plastic: Consumer Credit after the Crisis, Knowledge @ Wharton 07/08/2009 Polling the Polling Experts: How Accurate and Useful Are Polls These Days?, Knowledge @ Wharton 11/14/2007 Tip of the Spear: Leadership Lessons from the U.S.led Armed Forces in the Middle East, Knowledge @ Wharton 05/17/2006 Mining Data for Nuggets of Knowledge, Knowledge @ Wharton 12/10/1999