Academics
MS Project: New Statistical Methods
The typical length of a MS project will be 10 or more double-spaced pages.
- ABSTRACT: A brief summary (150 words) should state the purpose of the methods and the main approach.
- INTRODUCTION: This section should describe the purpose of research, and possibly the previous work the research builds on including:
- Justify why the research is needed.
- Briefly summary of the literature on previous work in this area.
- Present the objectives of the study.
- STATISTICAL METHODOLOGY: This section should describe the statistical approaches. The section should clarify the extent to which the proposed method has theoretical justification, and why it is superior to current methods.
- IMPLEMENTATION: This section describes the methods (usually software) used for implementation. Preferably, the programs used are attached as an Appendix.
- APPLICATION: At least one application of the method should be presented. Some details should be given on the particular setting, source of data, etc. first. The new methods should then be applied and compared to application of current approaches, if any exist.
- DISCUSSION: Discuss potential implications of the new methods. Describe and limitations and areas for further research.
- TABLES: If the new methods generate statistical thresholds for tests or measures of size / power, present some tables of representative values.
- FIGURES [May or may not be Helpful for a Masters Projects]
- CITED REFERENCES
MS Project: Study Protocol for a Clinical Trial
The typical length of a MS project is 10 pages double-spaced, with the following elements.
- ABSTRACT: A brief summary (100 - 250 words ) of the design appears at the front. This should state the purpose of research and its main design elements.
- BACKGROUND OF STUDY: Contains scientific information on why disease is important and why the treatment might work
- OBJECTIVES: Documents what the study proponents hope to achieve. Detail includes:
- PRIMARY QUESTION/RESPONSE VARIABLE
- SECONDARY QUESTION/RESPONSE VARIABLES
- SUBGROUP HYPOTHESES
- STUDY POPULATION: Describe the individuals to whom the treatment will be given in practice. Details should include:
- INCLUSION/EXCLUSION CRITERIA
- SAMPLE SIZE / POWER ESTIMATES This Section Required for a MS project.
- ENROLLMENT OF SUBJECTS: Describe how subjects will be identified, screened and enrolled. Include descriptions of
- INFORMED CONSENT
- ASSESSMENT OF ELIGIBILITY
- BASELINE INFORMATION
- RANDOMIZATION SCHEME This Section Required for a MS project.
- INTERVENTION: Describe the intervention and delivery as it will be given in practice, including the following elements:
- DESCRIPTION AND SCHEDULE
- MEASURES OF COMPLIANCE
- FOLLOW-UP VISIT DESCRIPTION AND SCHEDULE - Describe how all needed information will be obtained and how loss to follow up will be minimized.
- ASCERTAINMENT OF RESPONSE VARIABLES: Please provide details on the following areas:
- TRAINING
- DATA COLLECTION
- DATA MONITORING AND QUALITY CONTROL
- DATA ANALYSIS: Give the specific approaches that will be used for adjusted and unadjusted analysis of data, and in particular for the primary outcome of interest. This Section Required for a MS project.
- TERMINATION POLICY
- ORGANIZATION: This section is very important, since a great deal of the success of a protocol depends on management. Please include these elements:
- PARTICIPATING INVESTIGATORS: Verify that the appropriate people are doing the appropriate things.
- STUDY ADMINISTRATION, with committees and subcommittees, including the policy and data monitoring committee.
- CITED REFERENCES: Cite all articles and sources where background and other information was obtained.
NOTE: Protocols for studies that are not clinical trials may have slightly different formats.
MS Project: Statistical Analysis of Data
The typical length of a MS project is 10 pages double-spaced, with the following elements.
- ABSTRACT: A brief summary (100 - 250 words ) of research appears at the front. This should state the purpose of research and its main findings.
- INTRODUCTION: Describe the purpose of research, and possibly the previous work the research builds on, including:
- justifying why the research needed.
- summarizing the literature on previous work in this area.
- listing the objectives of the study.
- METHODS: Describe the conduct of the study, including how the data were collected, analyzed, etc, including:
- General study design (was it an experiment, a case-control, or cohort study, etc.)
- Description of the Study Participants or Objects: the study population, sampling frame, criteria for inclusion and exclusion, and the method for selecting study subjects.
- DATA COLLECTION: Describe variables, measurement techniques, and validity/reliability of instruments. One could also describe quality control procedures and/or methods used to minimize loss of information.
- STATISTICAL METHODS: A brief description of the statistical tests and software used to do the analysis.
- RESULTS: For studies involving people the results are often presented in the following order.
- Descriptive data: study population described according to demographic/socio-economic variables such as age, income, education, health status, etc.
- Crude (unadjusted) measures of Association: Relative risks from 2x2 tables, coefficients from univariate models, comparisons of group means by t-tests, etc.
- Stratified analyses and Simple Methods of Adjustment: For example, separate relative risks, t-tests, etc. by racial group.
- More complicated analyses with multivariate models: linear regression, logistic regression, proportional hazards, etc.
Analogous presentations of results would be made for other types of studies.
- DISCUSSION This section interprets the results and explains the implications and limitations. Often (but not always) this involves the following in this order:
- A brief summary of the findings.
- A short review of the literature, contrasting with the study findings [May not be Necessary for Most Masters Projects]
- Discussion of strengths and limitations of the study.
- Implications for policy. What impact could these findings have on the way things are done now? [May not be Necessary for Most Masters Projects]
- Suggestions for future studies. Could shortcomings of this study be improved on in future research. [May not be Necessary for Most Masters Projects]
- CITED REFERENCES
- TABLES [Usually any Masters Report of Data Analysis will have at least one table of important results]
- FIGURES [May or may not be Helpful for a Masters]
- APPENDIX [OPTIONAL] Students may want to attach a copy of the computer program used to do the data analysis.
MS Project
Statistics MS Project
The Statistics MS project is designed to demonstrated the ability to undertake planning and conducting statistical research, and communicating plans and results in writing.
The Masters project should be completed in one of the classes the student takes. Several core Statistics and Biostatistics MS classes give data analysis or study planning assignments which require writing a report that (depending on how well the student does) fulfills the Masters project requirements. Student should check with teachers of the course on whether a particular assignment of that class meets these requirements.
The format of a report depends on the type of statistical research, design, or analysis that is done for the project. Most Masters projects will fall into one of the following three categories.
- Statistical Analysis of Data Using statistical methods to analyze data for a focused problem of interest and communicating findings in a manner to advance the substantive field. The student is also expected to also write the programs used to analyze the data.
LINK TO TYPICAL FORMAT FOR STATISTICAL ANALYSIS OF DATA - Protocols for Statistical Research (Typically Clinical Trials) Development of study plans or draft study designs to be used to obtain approval or funding or to undertake applied statistical research. The student is also expected to use statistical software (or otherwise) to obtain power / sample size estimates.
LINK TO TYPICAL FORMAT FOR STUDY PROTOCOL - Development of Statistical Methods or New Applications Development of new statistical methods or application of current statistical methods to a new area. The student is expected to contribute substantially to the development and/or the application. [As this may be more challenging than the other two types of projects, it is not expected that many Masters Projects would involve this].
LINK TO TYPICAL FORMAT FOR NEW STATISTICAL METHOD/APPLICATION
The formats described above for each of these Types of Masters projects are suggestions, not strict requirements. The teacher of a class (in consultation with other faculty) has discretion on whether a written report fulfills the Masters Report requirement and Masters Projects that do not fall exactly into the above formats are possible. Still, any Masters Project should require students to undertake statistical research or planning and written communication at equivalent levels to these formats.
Schedule and Book List
Schedule and Book List for the Upcoming Semester
The Registrar's office makes available specific course schedules for specific terms. Our department maintains a list with instructors for the current semester and expected instructors for the upcoming semester.
Books for our courses are generally ordered through the Ferren Mall bookstore . From this link, choose the Ferren Mall bookstore, and then choose books and then textbooks, and request books for our department, coded as 960.
Typical Schedule
We hope to offer the following courses at the following standard times. Staffing issues sometimes force us to modify this schedule, and the Department of Statistics and Biostatistics often offers additional courses not on this regular rotation. More specific scheduling information is available here. The list below also contains times for some courses offered by other programs that are frequently of interest to our MS students. These courses are italicized. Times listed below are times when the courses have been offered in the past. The program in Statistics and Biostatistics has no control over the scheduling of these courses, and makes no claims that the times below are current.
Fall Schedule
Time | Monday | Tuesday | Wednesday | Thursday |
10:20 AM - 11:40 AM (2) | 592: Theory of Probability | 652: Advanced Theory of Statistics I | 592: Theory of Probability | 652: Advanced Theory of Statistics I |
12:00 - 1:20 PM (3) | 663: Regression Theory | Unavailable | 663: Regression Theory | |
1:40 PM - 3:00 PM (4) | 596: Intermediate Methods | Unavailable1 | 596: Intermediate Methods | Unavailable1 |
3:20 PM - 4:40 PM (5) | Unavailable | 681: Advanced Probability Theory II 198:513: Design and Analysis of Data Structures and Algorithms I |
693: Current Topics in Statistics | 681: Advanced Probability Theory II 198:513 Design and Analysis of Data Structures and Algorithms I |
5:00 PM - 6:20 PM (6) | 690: Special Topics3 | 690: Special Topics3 | ||
6:40 PM - 9:30 PM (7-8) | 584: Biostatistics I 590: Design of Experiments 591: Advanced Design of Experiments |
582: Introduction to the Methods and Theory of Probability 588: Data Mining |
553: Categorical Data Analysis 586: Interpretation of Data 540:585 Systems Reliability Engineering |
555: Methods of Nonparametric Statistics 563: Regression Analysis 540:580 Quality Management |
Spring Schedule
Time | Monday | Tuesday | Wednesday | Thursday |
10:20 AM - 11:40 AM (2) | 593: Theory of Statistics | 653: Advanced Theory Statistics II | 593: Theory of Statistics | 653: Advanced Theory Statistics II |
12:00 - 1:20 PM (3) | 654: Stochastic Processes3 | Unavailable | 654: Stochastic Processes3 | |
1:40 PM - 3:00 PM (4) | 667: Multivariate Statistics1 668: Bayesian Data Analysis2 |
690: Special Topics3 | 667: Multivariate Statistics1 668: Bayesian Data Analysis2 |
690: Special Topics3 |
3:20 PM - 4:40 PM (5) | Unavailable | 680: Advanced Probability Theory I | 693: Current Topics in Statistics | 680: Advanced Probability Theory I |
5:00 PM - 6:20 PM (6) | ||||
6:40 PM - 9:30 PM (7-8) | 540: Statistical Quality Control I 565: Time Series 580: Basic Probability Statistics 585: Biostatistics II 198:536: Machine Learning |
554: Applied Stochastic Processes 583: Methods of Statistical Inference 590: Design of Experiments |
576: Survey Sampling 586: Interpretation of Data 587: Interpretation of Data II |
542: Life Data Analysis 563: Regression Analysis 567: Applied Multivariate Analysis 198:513 Design and Analysis of Data Structures and Algorithms |
Summer Schedule
Time | Monday, Wednesday (1st) | Tuesday, Thursday (1st) | Monday, Wednesday (2nd) | Tuesday, Thursday (2nd) |
Evening | 563: Regression Analysis | 580: Basic Probability Statistics | 590: Design of Experiments | 540: Statistical Quality Control I 583: Methods of Statistical Inference |
1Offered in alternate years, starting with Calendar Year 2007
2Offered in alternate years, starting with Calendar Year 2008
3When offered.
Syllabi
- pdf 563_Mardekian.pdf (27 KB)
- pdf 565_Xiao.pdf (122 KB)
- pdf 567_Yang.pdf (81 KB)
- pdf 576_Naus.pdf (81 KB)
- pdf 580_Ramaswami.pdf (421 KB)
- pdf 583_Sackrowitz.pdf (20 KB)
- pdf 585_Hoover.pdf (39 KB)
- pdf 586_Agre.pdf (102 KB)
- pdf 590_Rojas.pdf (500 KB)
- pdf 654_Crane.pdf (63 KB)
- pdf 667_Tyler.pdf (64 KB)
- pdf 680_Gundy.pdf (18 KB)
- pdf 691_Prekopa.pdf (76 KB)
- pdf 958-535_Zhang.pdf (42 KB)
- pdf 958-565_Xiao.pdf (127 KB)
- pdf 958-583_Dicker.pdf (99 KB)
PhD Dissertation
Rutgers Statistics PhD Program Dissertation Information
The Graduate School--New Brunswick provides this style guide available for use in planning the thesis.
PhD Typical Plan
Sample PhD Program
Year 1
Fall | Spring | Summer |
---|---|---|
592: Theory of Probability | 654: Stochastic Processes | Qualifying Exam Review Course |
596: Advanced Applied Statistics I | 597: Advanced Applied Statistics II | |
593: Theory of Statistics I | 594: Theory of Statistics II |
Year 2
Fall | Spring | Summer |
---|---|---|
652: Advanced Theory of Statistics I | 653: Advanced Theory of Statistics II | |
680: Advanced Theory of Probability | 690: Special Topics in Statistics | |
663: Regression Theory |
Year 3
Students 3rd year and beyond take courses as necessary, depending on their interests and thesis topic. Most students will register for 702 (research credit) or 682 (reading course/independent study). Students must also register for 3 semesters of 693 (seminar) before graduation.
PhD Degree Program
PhD Program Description
The degree of Doctor of Philosophy is a research degree, conferred in recognition of marked ability and scholarship and high scholastic attainment and original research in Statistics and Biostatistics. The degree is conferred after successful completion of an acceptable thesis summarizing substantial results of original research work relevant to Statistics and/or Biostatistics. Thesis work will be carried on with the general guidance and under the supervision of the candidate's Thesis Advisor.
Areas of specialization for research include any topic suitable for research in applied or theoretical statistics, including statistical inference, estimation theory, hypothesis testing, decision theory, empirical Bayes and Bayes methods, regression analysis, analysis of variance, statistical computing, experimental design, multivariate analysis, nonparametric statistics, sequential analysis, quality control theory, time series analysis, applied probability, stochastic processes, and probability theory.
Formal Credit Requirements
Ph.D. candidates must ordinarily have at least 72 semester-hours of approved graduate credits. This will generally consist of 48 hours of course credits and the remainder of the 24 hours as research credits. Ph.D. students are urged to spend at least one full academic year in residence on campus, although there is no formal residency requirement.
Transfer of Credits
Up to 30 credits of such acceptable credits may be permitted to be applied for the Ph.D. degree. This is subject to individual consideration.
A Guide for Courses and Electives for Ph.D. in Applied and Mathematical Statistics
Required Courses:
587 Interpretation of Data II
592 Theory of Probability
593 Theory of Statistics
652-653 Advanced Theory of Statistics I and II
663 Regression Theory
680-681 Advanced Probability Theory I and II (or two other 600-level courses approved by the Graduate Director)
Two additional 600 level courses in Statistics
693 Current Topics in Statistics, 3 semesters
Electives:
540-541 Quality Control I and II
542 Life Data Analysis
545 Statistical Practice
553 Categorical Data Analysis
554 Applied Stochastic Processes
555-655 Nonparametric Statistics & Advanced Nonparametric Statistics
563 Regression Analysis
565 Applied Time Series Analysis
567 Applied Multivariate Analysis
575 Acceptance Sampling Theory
576 Survey Sampling
584-585 Biostatistics I and II
586 Interpretation of Data I
588 Data Mining
590-591 Design of Experiments & Advanced Design of Experiments
595 Intermediate Probability
654 Stochastic Processes
664 Advanced Topics in Regression and Analysis of Variance
667 Multivariate Analysis
687-688 Seminar in Applied and Mathematical Statistics
689 Sequential Methods
690-691 Special Topics (topics on rotating basis): Large Sample Theory, Time Series
Bayesian Statistics, Robustness, Sequential Analysis.
Examination Requirement
A precondition to being formally admitted as a Ph.D. candidate is that the student pass the Ph.D. Examinations, the purpose of which is to determine the breadth of the student's mastery of their major and minor fields. The first of these exams is a comprehensive written qualifying exam on all first-year course material. The qualifying exam is taken before the second-year and consists of three parts: one exam each on mathematical statistics, probability theory, and applied statistics. Progress toward the Ph.D. is monitored throughout the second and third years, culminating in a written thesis proposal (due by the end of the 2nd semester of the third year) and an oral thesis proposal (presented no later than the 1st semester of the fourth year).