Ph.D. student lectures.

Program and Course Information

Ph.D. Program

A total of 72 credits is required for the doctoral degree. These must include:

  • at least 18 credits in dissertation research.
  • at least 36 credits in degree courses. (This can be reduced only if some course requirements are transferred.)
  • 6 credits in the early research requirements.

Select a program to learn more about its additional requirements and course information.

Relevant Courses from Other Schools

Please consult the websites of the schools and departments for the most updated information on relevant courses.

Course Descriptions and Syllabi

26:120:560 - Effective College Teaching

Recent syllabus by Lion Gardiner

Psychology Department, Rutgers-Newark

The department is recruiting in social psychology and may expand its teaching in this area. Visit the website ›

26:830:512 Decision Making

26:830:545 Research Design

26:830:595 Research Method Psychology

26:830:613 Introduction to Social Conflict

Every spring.
May be useful for Organization Management students.

26:830:667 Cognitive Processes

Every fall.
May serve as a minor course for behavioral students in Accounting.

Department of Public Administration, Rutgers-Newark

26:834:607 Doctoral Research Methods I

Every spring.

26:834:608 Doctoral Research Methods II

Every fall.

26:834:609 Qualitative Methods in Public Administration

Every spring.

Survey Research

Department of Economics, Rutgers-Newark

26:220:515 Economics of the Public Sector

26:220:518 International Economics I

26:220:536 Health Economics

26:220:685 Development Economics

Department of Computer and Information Science, NJIT

Math 698 Sampling Theory

(Really a course in sample surveys.)
3 credits
Prerequisite: Math 662 or equivalent. (Professor Bhattacharjee stated that our course 26:960:577 Introduction to Linear Statistical Models, which all our students take in their first semester, is enough to qualify students to take this course.)
Role of sample surveys. Sampling from finite populations. Sampling designs, the Horowitz-Thompson estimator of the population mean. Different sampling methods, simple random sampling, stratified sampling, ratio and regression estimates, cluster sampling, systematic sampling.

Math 644 Regression Analysis Methods

3 credits
Prerequisite: Math 661 or equivalent.
Regression models and the least squares criterion. Simple and multiple linear regression. Regression diagnostics. Confidence intervals and tests of parameters, regression and analysis of variance. Variable selection and model building. Dummy variables and transformations, growth models. Other regression models such as logistic regression. Using statistical software for regression analysis. This course usually uses JMP, which is a menu-driven version of SAS.
Although this course overlaps with our 26:960:577 Introduction to Linear Statistical Models, it could be very useful for students who need to see this material for a second time.

Math 646 Time Series Analysis

3 credits
Prerequisite: Math 661 or, permission of instructor. Time series models, smoothing, trend and removal of seasonality. Naive forecasting models, stationarity and ARMA models. Estimation and forecasting for ARMA models. Estimation, model selection and forecasting of nonseasonal and seasonal ARIMA models.

Math 661 Applied Statistics

3 credits
Prerequisite: undergraduate calculus. Role and purpose of applied statistics. Data visualization and use of statistical software used in course. Descriptive statistics, summary measures for quantitative and qualitative data, data displays. Modeling random behavior: elementary probability and some simple probability distribution models. Normal distribution. Computational statistical inference: confidence intervals and tests for means, variances, and proportions. Linear regression analysis and inference. Control charts for statistical quality control. Introduction to design of experiments and ANOVA, simple factorial design and their analysis.
This course would be excellent for students who come into our program without the background expected for students in 26:960:577 Introduction to Linear Statistical Models.

Math 662 Probability Distributions

3 credits
Prerequisite: a background in undergraduate statistics or permission of instructor. Probability, conditional probability, random variables and distributions, independence, expectation, moment generating functions, useful parametric families of distributions, transformation of random variables, order statistics, sampling distributions under normality, the central limit theorem, convergence concepts and illustrative applications.

Math 668 Probability Theory

3 credits
Prerequisite: Math 662 or equivalent. Introduction to measure theory and integration, axiomatic probability, random variables, distribution function, expectation, independence, modes of convergence, characteristic functions, Laplace-Stieltjes transforms, sums of identically distributed random variables, conditional expectation, martingales.
This course is at a slightly higher level than our 26:960:575 Introduction to Probability and might be useful for students in finance or management science.

Math 691 Stochastic Processes with Applications

3 credits
Prerequisite: Math 662 or equivalent. Renewal theory, renewal reward processes and applications. Homogeneous, non-homogeneous and compound Poisson processes with illustrative applications. Introduction to Markov chains in discrete and continuous time with selected applications.

Math 699 Design and Analysis of Experiments

3 credits
Prerequisite: Math 662 or equivalent. Statistically designed experiments and their importance in data analysis, industrial experiments. Role of randomization. Fixed and random effect models and ANOVA, block design, latin square design, factorial and fractional factorial designs and their analysis.

Math 761 Statistical Reliability Theory and Applications

3 credits
Prerequisite: Math 662 or permission of instructor. Survival distributions, failure rate and hazard functions, residual life. Common parametric families used in modeling life data. Introduction to nonparametric aging classes. Coherent structures, fault tree analysis, redundancy and standby systems, system availability, repairable systems, selected applications such as software reliability.

Statistics Department, Rutgers-New Brunswick

Visit the website ›

26:960:580 Stochastic Processes

16:960:567 Applied Multivariate Analysis

Every spring.
May substitute for 26:630:670.

26:960:575 Probability

    26:960:577 Linear Statistical Models

    16:960:590 Design of Experiments

    Every fall.

    16:960:592 Theory of Probability

    Every fall.
    May substitute for 26:960:575 for Management Science majors.

    16:960:593 Theory of Statistics

    Every spring.
    May serve as a minor course for Management Science majors.

    School of Communication, Information, and Library Studies (SCILS), Rutgers-New Brunswick

    Visit the website ›

    16:194:546 Management and Information Technology

    School of Management and Labor Relations (SMLR), Rutgers-New Brunswick

    Visit the website ›

    16:545:610 Economics for Industrial Relations and Human Resources

    16:545:611 Seminar in Industrial Relations

    16:545:612 Seminar in Human Resources

    Rutgers Center for Operations Research

    Department of Economics, Rutgers-New Brunswick

    Please note: Links to recent syllabi are provided where possible. In some cases, the link goes to the web site for the individual faculty member, where the syllabus is maintained. In other cases, the link allows you to download the syllabus. Other syllabi are available in the Program Office.

    These syllabi are provided as information to potential applicants. They should also help current students make their individual study plans. But they are subject to change. Students should not buy books or make other plans related to a course until they have confirmed with the instructor that they have an up-to-date syllabus for the semester in which they are taking the course.