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
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
- Recent syllabus by Mauricio Delgado
26:830:545 Research Design
26:830:595 Research Method Psychology
- Recent syllabus by Steve Hanson
26:830:613 Introduction to Social Conflict
May be useful for Organization Management students.
26:830:667 Cognitive Processes
May serve as a minor course for behavioral students in Accounting.
Department of Public Administration, Rutgers-Newark
26:834:607 Doctoral Research Methods I
26:834:608 Doctoral Research Methods II
26:834:609 Qualitative Methods in Public Administration
- Recent Syllabus
- Course description by Dr. Van Ryzin
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
- Recent syllabus by Professor Julia Schwenkenberg
Department of Computer and Information Science, NJIT
Math 698 Sampling Theory
(Really a course in sample surveys.)
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
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
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
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
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
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
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
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
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
26:960:580 Stochastic Processes
16:960:567 Applied Multivariate Analysis
May substitute for 26:630:670.
- Recent syllabus by Zachary Stoumbos
- Previous syllabus by Zachary Stoumbos
26:960:577 Linear Statistical Models
- Recent syllabus by Farid Alizadeh
16:960:590 Design of Experiments
16:960:592 Theory of Probability
May substitute for 26:960:575 for Management Science majors.
16:960:593 Theory of Statistics
May serve as a minor course for Management Science majors.
School of Communication, Information, and Library Studies (SCILS), Rutgers-New Brunswick
16:194:546 Management and Information Technology
School of Management and Labor Relations (SMLR), Rutgers-New Brunswick
16:545:610 Economics for Industrial Relations and Human Resources
16:545:611 Seminar in Industrial Relations
16:545:612 Seminar in Human Resources
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.