Wei Wang

Research Interests

Causal Inference, Mixed effects models, Observational studies, Smoothing under shape constraints, Survival analysis.

Publications

https://www.ncbi.nlm.nih.gov/myncbi/1t5GUSlrcz4ccb/bibliography/public/

Current Courses (Fall 2026)

• 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.

  STAT4220001 ( Syllabus )

  STAT4220002 ( Syllabus )

• STAT5150 - Adv Stat Inference I

  STAT 5150 is aimed at first-year Ph.D. students and builds a good foundation in statistical inference from the first principles of probability.

  STAT5150001 ( Syllabus )

• 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.

  STAT7220001 ( Syllabus )

All Courses

• HPR6040 - Intro Stat Health Policy

  This is the first semester of a two-semester sequence. It is an introductory statistics course covering descriptive statistics, probability, random variables, estimation, hypothesis testing, and confidence intervals for normally distributed and binary data. The second semester stresses regression models. Permission needed from instructor to enroll.

• PSYC6120 - Int To Nonp & Loglin Mod

  An applied graduate level course for students who have completed an undergraduate course in basic statistical methods. Covers two unrelated topics: loglinear and logit models for discrete data and nonparametric methods for nonnormal data. Emphasis is on practical methods of data analysis and their interpretation. Primarily for doctoral students in the managerial, behavioral, social and health sciences. Permission of instructor required to enroll.

• STAT0001 - Intro To Stat & Data Sci

  In this course, we will learn introductory statistics using R with a focus on the application of statistical thinking to business problems. We will learn basic statistical concepts such as mean, variance, quantiles and hypothesis testing, and basic R programming for data management and analysis. We will work with traditional R's data, frame structure as well as the modern tibbles structure. Prerequisite: Percentages, average, powers, exponential, linear equation of a line, polynomials.

• 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.

• STAT4230 - Machine Learning in Business

  This course introduces students to machine learning techniques used in business applications. The main topics include: cross validation, variable selection procedures, shrinkage methods such as lasso, logistic regression, k-nearest neighbors, ROC curves and confusion matrix, trees, kernel based learning, resampling techniques, random forests, boosting, neural networks & deep learning, matrix methods including singular value decomposition (SVD) and its application in principal component analysis (PCA), and some unsupervised methods such as k-means and density based clustering. Students will learn to apply these methods in a wide range of settings such as marketing and finance, and will gain hands-on experience through class assignments and competitions. This course may be taken concurrently with the prerequisite with instructor permission.

• STAT5010 - Int To Nonp & Loglin Mod

  An applied graduate level course for students who have completed an undergraduate course in basic statistical methods. Covers two unrelated topics: loglinear and logit models for discrete data and nonparametric methods for nonnormal data. Emphasis is on practical methods of data analysis and their interpretation. Primarily for doctoral students in the managerial, behavioral, social and health sciences. Permission of instructor required to enroll.

• STAT5150 - Adv Stat Inference I

  STAT 5150 is aimed at first-year Ph.D. students and builds a good foundation in statistical inference from the first principles of probability.

• STAT5160 - Adv Stat Inference II

  STAT 5160 is a natural continuation of STAT 5150, and the main focus is on asymptotic evaluations and regression models. Time permitting, it also discusses some basic nonparametric statistical methods.

• 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.

• 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.