Abba M. Krieger

Past Courses

• STAT101 - INTRO BUSINESS STAT

  Data summaries and descriptive statistics; introduction to a statistical computer package; Probability: distributions, expectation, variance, covariance, portfolios, central limit theorem; statistical inference of univariate data; Statistical inference for bivariate data: inference for intrinsically linear simple regression models. This course will have a business focus, but is not inappropriate for students in the college.

• 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.

• 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.

• 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 real-life 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.

• STAT991 - SEM IN ADV APPL OF STAT

  This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

• WH 299 - HONORS THESIS

  This seminar takes place over two semesters and provides students with the skills to perform their own research under the guidance of a Wharton faculty member. At the conclusion of the fall semester, students will produce a thesis proposal including literature review, significance of the research, methodology, and exploratory data if relevant. Throughout the fall semester faculty guests from a range of disciplines will present on their research in class, highlighting aspects that are relevant to the work students are engaging in at that point. During the second semester, students will collect and analyze data and write up the results in close collaboration with their faculty mentor. At the end of the spring semester, each student will present their research in a video presentation. Throughout the course, students will work individually, in small groups, and under the mentorship of a Wharton faculty member. The goal is to becomes capable independent researchers who incorporate feedback and critical (self-) analysis to take their research to the next level.

• Wharton MBA Core Award, 2010
• Journal of Advertising Research – Award for Best Paper of 2007 (received with co-authors Leonard Lodish and Ye Hu), 2008 Description

  “An Analysis of Real World TV Advertising Tests: A 15-Year Update”, from JAR volume 47, issue 3, has been voted by the Journal of Advertising Research Editorial Board as the JAR Best Paper of 2007.The JAR Best Paper Prize was introduced this year to recognize the contribution of JAR authors to furthering the industry’s knowledge of advertising research.

• David W. Hauck Award for Outstanding Teaching in the Undergraduate Division, 1996
• Undergraduate Division Excellence in Teaching Award, 1996 Description

  1991, 1995, 1996

• David W. Hauck Award for Outstanding Teaching in the Undergraduate Division, 1996
• Fellow, American Statistical Association, 1992
• Fellow, American Statistical Association, 1992
• Alpha Kappa Psi Award, Journal of Marketing, 1991
• Alpha Kappa Psi Award, Journal of Marketing, 1991
• Lindback Award for Distinguished Teaching, University of Pennsylvania, 1978
• Lindback Award for Distinguished Teaching, University of Pennsylvania, 1978
• Helen Kardon Moss Anvil Award for Teaching Excellence in the Graduate Division, 1977
• Helen Kardon Moss Anvil Award for Teaching Excellence in the Graduate Division, 1977

Knowledge @ Wharton

• A Farewell to Two Business Visionaries, Knowledge @ Wharton - 10/10/2012
• It’s Not Easy Being Paul Green, Knowledge @ Wharton - 11/08/2000