College of LS&A

Winter '01 Graduate Course Guide

Note: You must establish a session for Winter Term 2001 on wolverineaccess.umich.edu in order to use the link "Check Times, Location, and Availability". Once your session is established, the links will function.

Courses in Statistics


This page was created at 7:58 AM on Tue, Jan 30, 2001.

Winter Term, 2001 (January 4 April 26)

Open courses in Statistics
(*Not real-time Information. Review the "Data current as of: " statement at the bottom of hyperlinked page)

Wolverine Access Subject listing for STATS

Winter Term '01 Time Schedule for Statistics.


STATS 350(250/402). Introduction to Statistics and Data Analysis.

Section TWO (2) WEDNESDAY EVENING MIDTERM EXAMINATIONS FOR STATISTICS 402 ON 02/14 AND 3/28, 6-8 P.M.

Instructor(s): Brenda Gunderson (bkg@umich.edu)

Prerequisites: No credit granted to those who have completed or are enrolled in Econ. 404 or 405, or Stat. 250, 265, 311, 400, 402, 405, or 412.

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/class/stat350/

In this course students are introduced to the concepts and applications of statistical methods and data analysis. Statistics 250 has no prerequisite and has been elected by students whose mathematics background includes only high school algebra. Examples of applications are drawn from virtually all subject-matter. The course format includes lectures (3 hours per week) and a laboratory (1.5 hours per week). The laboratory section deals with the computational aspects of the course and provides a forum for review of lecture material. For this purpose, students are introduced to the use of a statistical analysis-computer package. Course evaluation is based on a combination of two midterm examinations, a final examination, weekly homework, and lab participation.

This course was previously Stats 402. Advanced undergraduates and graduate students should take the new course, Stats 400.

Homework There will be weekly homework assignments and weekly computer lab work. Problems assigned (and their corresponding solutions) will be posted and available on the web for printing. Scientific Notebook Viewer is needed for viewing and printing solutions. Download instructions are available on the web (under Latest News).There are two semester exams and a Final exam.

Grading Policy: Performance on exams will account for 90% of your final grade and homework/lab attendance and participation will account for 10%.

Textbook: (required) Introduction to the Practice of Statistics Authors: Moore and McCabe, Freeman, 1999
Computer Modules: (required) Statistics 350 (250) Workbook, Winter 2001 Hayden-McNeil Publishing Inc., 2000 ISBN: 0-7380-0405-7
SPSS 6.1 (Student Version) (Mac)
SPSS 6.1 (Student Version) (Word)

Lecture Notes: (optional used with Prof. Gunderson and Prof. Kapatou's sections only) Winter 2001 Hayden-McNeil Publishing Inc., 2000 ISBN: 0-7380-0323-9

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: 1

STATS 400. Applied Statistical Methods.

Section 001.

Instructor(s): Derek Bingham (dbingham@umich.edu)

Prerequisites: High School Algebra. No credit granted to those who have completed or are enrolled in Econ. 404 or 405, or Stat. 250, 350, 265, 402, 405, or 412. (4).

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~dbingham/Stat400/index.html

This course is aimed at advanced undergraduate students and graduate students from disciplines outside of Statistics. The course will introduce students to a broad range of applied statistical methods involved in data collection, analysis and visualization. Emphasis will be placed on using statistical methods to answer real-world problems. Statistics and the scientific method; observational study versus designed experiment; visualization; introduction to probability; statistical inference; confidence intervals; one-sample tests of hypothesis; two-sample problems; analysis of variance (ANOVA); blocked designs; tests for association and independence (chi-square tests); regression and correlation; and non-parametric tests. Course format includes lectures (3 hours per week) and a laboratory (1.5 hours per week).

TEXTBOOK:
Statistics: Principles and Methods
Johnson/Bhattacharyya
4th Edition
ISBN: 0-471-38897-1
Wiley.

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STATS 400. Applied Statistical Methods.

Section 002.

Instructor(s): Alexandra Kapatou

Prerequisites: High School Algebra. No credit granted to those who have completed or are enrolled in Econ. 404 or 405, or Stat. 250, 350, 265, 402, 405, or 412. (4).

Credits: (4).

Course Homepage: No Homepage Submitted.

No Description Provided

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STATS 403. Introduction to Statistics and Data Analysis II.

Section 001.

Instructor(s): Thomas Ryan (tpryan@umich.edu)

Prerequisites: Stat. 350 (or 402). (4).

Credits: (4).

Course Homepage: No Homepage Submitted.

Intermediate topics in multiple linear regression and the analysis of variance, stressing applications: least squares estimates, test of hypotheses, prediction analysis, residual analysis, multicollinearity, and the variable selection techniques; fixed and random effects models in ANOVA; multiple comparisons, randomized blocks, Latin squares, nested and hierarchical designs; and robust procedures, as time permits. Three hours of lecture supplemented by one and one-half hours of laboratory.

TEXTBOOK:
Modern Regression Methods
Thomas P. Ryan
Wiley
ISBN: 0-471-52912-5.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 405/Econ. 405. Introduction to Statistics.

Section 001.

Instructor(s): Thomas Ryan (tpryan@umich.edu)

Prerequisites: Math. 116 or 118. Juniors and seniors may elect this course concurrently with Econ. 101 or 102. No credit granted to those who have completed or are enrolled in Stat. 265, 311 or 412. Students with credit for Econ. 404 can only elect Stat. 405 for 2 credits and must have permission of instructor. (4).

Credits: (4).

Course Homepage: No Homepage Submitted.

See Economics 405.001.

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STATS 408. Statistical Principles for Problem Solving: A Systems Approach.

Section 001 Meets with Statistics 170.001.

Instructor(s): Edward D Rothman (erothman@umich.edu)

Prerequisites: High School Algebra. No credit granted to those who have completed or are enrolled in Statistics 170. (4).

Credits: (4).

Course Homepage: No Homepage Submitted.

Our purpose is to help students use quantitative reasoning to facilitate learning. Specifically, we introduce statistical and mathematical principles, and then use these as analogues in a variety of real world situations. The notion of a system, a collection of components that come together repeatedly for a purpose, provides an excellent framework to describe many real world phenomena and provides a way to view the quality of an inferential process.

Reading Materials Include: "The New Economics," 2nd Edition; W. Edwards Deming; "The Fifth Discipline," Peter Senge; "The Goal," Eliyahu Goldratt; "Theory of Constraints," Eliyahu Goldratt. Evaluation is focused on clear writing that illustrates understanding of the theory by providing new applications of the theory. Points are obtained from four activities: (1) a journal (max 20 points); (3) test score (max 30 points); (4) discussion section leader bonus (max 5 additional points).

TEXTBOOK:
Theory of Constraints
E. Goldratt
Northriver Press

The Goal
E. Goldratt
Northriver Press

The Fifth Discipline
Peter M. Senge
Doubleday Currency

The New Economics for Industry Government Education
W. Edwards Deming
2nd Edition
MIT

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: 1

STATS 412. Introduction to Probability and Statistics.

Section 001.

Instructor(s): P. Jegenathan (jegan@umich.edu)

Prerequisites: Prior or concurrent enrollment in Math. 215 and CS 183. No credit granted to those who have completed or are enrolled in Econ. 405, or Stat. 265, 311, 350, 400, or 405. One credit granted to those who have completed Stat. 250 or 402. (3).

Credits: (3).

Course Homepage: http://www.stat.lsa.umich.edu/~wangxiao/412.html

The objectives of this course are to introduce students to the basic ideas of probability and statistical inference and to acquaint students with some important data analytic techniques, such as regression and the analysis of variance. Examples will emphasize applications to the natural sciences and engineering. There will be regular homework, two midterms, and a final exam.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 425/Math. 425. Introduction to Probability.

Section 001.

Instructor(s): P Jeganathan (jegan@umich.edu)

Prerequisites: Math. 215, 255, or 285. (3).

Credits: (3).

Course Homepage: No Homepage Submitted.

Sample spaces and axiomatic probability; elementary combinatorics; conditional probability and independence; random variables; probability distributions, including binomial, Poisson, Gamma, and normal; expectation, mean and variance; moment generating functions; the law of large numbers; central limit theorem.

TEXTBOOK:
A First Course in Probability
Sheldon Ross
5th Edition
Prentice-Hall
ISBN: 0-137-46314-6.

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STATS 425/Math. 425. Introduction to Probability.

Section 002, 003, 006.

Prerequisites: Math. 215, 255, or 285. (3).

Credits: (3).

Course Homepage: No Homepage Submitted.

See Mathematics 425.002.

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STATS 425/Math. 425. Introduction to Probability.

Section 004, 005.

Instructor(s): Yanhong Wu

Prerequisites: Math. 215, 255, or 285. (3).

Credits: (3).

Course Homepage: No Homepage Submitted.

Sample spaces and axiomatic probability; elementary combinatorics; conditional probability and independence; random variables; probability distributions, including binomial, Poisson, Gamma, and normal; expectation, mean and variance; moment generating functions; the law of large numbers; central limit theorem.

TEXTBOOK:
A First Course in Probability
Sheldon Ross
5th Edition
Prentice-Hall
ISBN: 0-137-46314-6.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 425/Math. 425. Introduction to Probability.

Section 006.

Instructor(s): P Jeganathan

Prerequisites: Math. 215, 255, or 285. (3).

Credits: (3).

Course Homepage: No Homepage Submitted.

No Description Provided

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STATS 426. Introduction to Mathematical Statistics.

Section 001.

Instructor(s): Michael Woodroofe (michaelw@umich.edu)

Prerequisites: Stat. 425. (3).

Credits: (3).

Course Homepage: http://www.stat.lsa.umich.edu/~dhkim/426.html

This course covers the basic ideas of statistical inference, including sampling distributions, estimation, confidence intervals, hypothesis testing, regression, analysis of variance, nonparametric testing, and Bayesian inference. The sequence of Statistics 425/426 serves as a prerequisite for more advanced Statistics courses, regular homework and a final exam.


Random Variables
Joint Distributions
Induced Distributions
Expectation
The Law of Large Numbers
The Central Limit Theorem
Simulation
Populations and Samples
The Chi-squared, t, and F Distributions
Estimation: The Method of Moments
Maximum Likelihood Estimation
More on Maximum Likelihood Estimation
Bias, Variance, and MSE
The Cramer Rao Inequality
Exponential Families and Sufficiency
Confidence Intervals
Approximate Confidence Intervals
The Bootstrap
Asymptotics of the MLE
Tests and Confidence Intervals
Neyman Pearson
Likelihood Ratio Tests
Chi-Squared Tests
Goodness of Fit Tests
The Sample Distribution Function
Decision Analysis
Bayesian Inference
The Two Sample Problem
More on the Two Sample Problem
Rank Tests
One Way ANOVA
Simultaneous Confidence
Two Way ANOVA
Categorical Data
Simple Linear Regression
Multiple Regression

Text: Mathematical Statistics and Data Analysis by John Rice

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 430. Applied Probability.

Section 001.

Prerequisites: Stats. 425. (3).

Credits: (3).

Course Homepage: No Homepage Submitted.

No Description Provided

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STATS 466/IOE 466/Manufacturing 466. Statistical Quality Control.

Section 001.

Instructor(s): Shiyu Zhou (zhous@umich.edu)

Prerequisites: Stat. 265 and Stat 403 or IOE 366. (4). CAEN lab access fee required for non-Engineering students.

Credits: (4).

Lab Fee: CAEN lab access fee required for non-Engineering students.

Course Homepage: http://www.engin.umich.edu/class/ioe466/

Design and analysis of procedures for forecasting and control of production processes. Topics include: attribute and variables sampling plans; sequential sampling plans; rectifying control procedures; charting, smoothing, forecasting, and prediction of discrete time series.

Text: "Introduction to Statistical Quality Control", 3rd edition, D.C. Montgomery, Wiley & Sons, 1996. "Lecture Notes Coursepack", Art and Architecture Copy Center

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 480. Survey Sampling Techniques.

Section 001.

Instructor(s): Thomas Ryan (tpryan@umich.edu)

Prerequisites: Stat. 350 (or 402). (4). Graduate credit for students outside the Stat. department.

Credits: (4).

Course Homepage: No Homepage Submitted.

Course will introduce students to basic ideas in survey sampling, moving from motivating examples to abstraction to populations, variables, parameters, samples and sample design, statistics, sampling distributions, Horvitz-Thompson estimators, basic sample designs (simple random, cluster, systematic, stratified, multiple state), various errors and biases, special topics. Three hours lecture and 1.5 hour laboratory session each week.

TEXTBOOK:
Sampling: Design and Analysis
Sharon L. Lohr
Duxbury
ISBN: 0-534-35361-4

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 501. Applied Statistics II.

Section 001.

Instructor(s): Julian J Faraway (faraway@umich.edu)

Prerequisites: Stat. 500. (3).

Credits: (3).

Course Homepage: http://www.stat.lsa.umich.edu/~faraway/stat501/

Generalized Linear Models, Analysis of binary and categorical data, Log-linear models, Random and mixed effects models, Smoothing and non-parametric regression, Generalized Additive models, Regression and classification trees, Neural Networks. Course pack. The following books are recommended "Generalized Linear Models" by Nelder & McCullagh (2nd Ed, Chapman Hall), "Applying Generalised Linear Models" by Lindsey (Springer), "Modelling Binary Data" by Collett (Chapman Hall) "Categorical Data Analysis" by Agresti (Wiley), "Generalized Additive Models" by Hastie & Tibshirani (Chapman Hall)

Assessment: Assignments worth 30%; Midterm worth 30%; Final worth 40%.

All examinations are open book.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 511. Mathematical Statistics II.

Section 001.

Instructor(s): Robert W Keener (keener@umich.edu)

Prerequisites: Stat. 510. (3).

Credits: (3).

Course Homepage: http://www.stat.lsa.umich.edu/~keener/511/index.html

Topics covered will include: hypothesis testing and related topics such as size, power, similarity and optimality properties. Likelihood ratio tests, generalized likelihood ratio tests, decision theory, and Bayes approaches. Sequential procedures, large sample theory, and various other topics.

TEXTBOOK:
Course Notes

Testing Statistical Hypothesis [recommended]
E. L. Lehmann
2nd Edition
Springer-Verlag
ISBN: 0-387-94919-4

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 525/Math. 525. Probability Theory.

Section 001.

Prerequisites: Math. 450 or 451. Students with credit for Math. 425/Stat. 425 can elect Math. 525/Stat. 525 for only one credit. (3).

Credits: (3).

Course Homepage: No Homepage Submitted.

See Mathematics 525.001.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 526/Math. 526. Discrete State Stochastic Processes.

Section 001.

Prerequisites: Stat. 525 or EECS 501. (3).

Credits: (3).

Course Homepage: No Homepage Submitted.

See Statistics 526.001.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 531/Econ. 677. Analysis of Time Series.

Section 001.

Instructor(s): E Philip Howrey (eph@umich.edu)

Prerequisites: Stat. 426. (3).

Credits: (3).

Course Homepage: No Homepage Submitted.

Models and methods for univariate and multivariate discrete- time stochastic processes; estimation, testing, and forecasting; frequency domain methods; applications of the Kalman filter.

TEXTBOOK:
Time Series Analysis
Hamilton
1994 Edition
Princeton University Press
ISBN: 0-691-04289-6

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 547/Math 547. Probabilistic Modeling in Bioinformatics.

Section 001.

Prerequisites: Stat 425 or Biol 427 or Biochem 45; basic prgramming skills desireable, Instructor permision." Graduate standing. (3).

Credits: (3).

Course Homepage: No Homepage Submitted.

See Mathematics 547.001.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 548/Math. 548. Computations in Probabilistic Modeling in Bioinformatics.

Section 001.

Prerequisites: "Math/Stat 425 or Biol 427 or Biochem 45; basic prgramming skills desireable, Instructor permision." Graduate standing. (1).

Credits: (1).

Course Homepage: No Homepage Submitted.

See Mathematics 548.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 570/IOE 570 Experimental Design.

Section 001.

Instructor(s): Derek Bingham (dbingham@umich.edu)

Prerequisites: Stat 500 or background in regression. Graduate standing. (3).

Credits: (3).

Course Homepage: No Homepage Submitted.

Basic topics and ideas in the design of experiments: randomization, blocking, replication; one-way and multi-way layout; Latin and Graeco-Latin squares; split-plot techniques; factorial experiments; fractional factional designs; robust parameter design.

TEXTBOOK:
Experiments: Planning, Analysis and Parameter Design Optimization
Wu/Hamada
2000 Edition
Wiley
ISBN: 0-471-25511-4

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 580/Soc. 717/Biostat. 617 (Public Health). Methods and Theory of Sample Design.

Section 001.

Instructor(s): James M Lepkowski (jimlep@umich.edu)

Prerequisites: Three or more courses in statistics and preferably a course in methods of survey sampling. (3).

Credits: (3).

Course Homepage: https://coursetools.ummu.umich.edu/2001/winter/biostat/617/001.nsf

Methods and Theory of Sample Design is concerned with the theory underlying the methods of survey sampling widely used in practice. It covers the basic techniques of simple random sampling, stratification, systematic sampling, cluster and multi-stage sampling, and probability proportional to size sampling. It also examines methods of variance estimation for complex sample designs, including the Taylor series expansion method, balanced repeated replications, and jackknife methods. It will cover several specialized topics, including stratification and subclasses, multi-phase or double sampling, ratio estimation, selection with unequal probabilities without replacement, non-response adjustments, imputation, and small area estimation. The course will examine both the practical applications of the sampling techniques presented as well as the theory supporting the methods.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 606. Statistical Computing.

Section 001.

Instructor(s): Kerby Shedden (kshedden@umich.edu)

Prerequisites: Calculus, Linear Algebra, some knowledge of Probability and Statistics. Graduate standing. (3).

Credits: (3).

Course Homepage: http://www.stat.lsa.umich.edu/~kshedden/Courses/Stat606/index.html

This course aims to give an overview of techniques in numerical analysis that are useful in the advanced practice of statistics. The course is roughly divided into three parts: evaluation of special functions, numerical linear algebra (linear solvers, matrix factorizations, eigenvalue problems), optimization (unconstrained methods, simplex method, active set methods, penalty function methods, combinatorial optimization), and simulation (importance and rejection sampling, Markov chain methods, exact methods). The course will cover some theoretical issues, but will primarily focus on the design and implementation of algorithms.

Coursework: 4 problem sets, each contributing 25% to the final grade. Programming: You will need to program to do the problem sets. You can use any programming language that you like, but you must do at least one problem set in a language that is different from the language that you use to do the other problem sets. Some good language choices are:
Matlab (free implementation is Octave, reference manual, language overview).
R, S, or S+ (R homepage, brief introduction by J. Faraway).
Scheme (MIT Scheme)
Perl
Fortran
Java
C

TEXTBOOK:
Matrix Computations
Golub/Van Loan
Johns Hopkins University Press

The following three books may be useful:
Matrix Computations, G.H. Golub, C.F. Van Loan. The Johns Hopkins University Press, 1996.

Numerical Analysis for Statisticians, K. Lange. Springer, 1999.

Numerical Analysis FAQ. Numerical Recipes in (C, Fortran), W.H. Press et al. Cambridge University Press, 1996.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 610. Mathematical Statistics I.

Section 001 Some Tools from Asymptotic Theory

Instructor(s): Susan Murphy (samurphy@umich.edu)

Prerequisites: Math. 601 and 625. Graduate standing. (3).

Credits: (3).

Course Homepage: http://www.stat.lsa.umich.edu/~samurphy/classes.html

Course description: This year Stat 610 will be a survey of tools in asymptotic theory. This course provides tools for evaluating statistical procedures via their large sample properties. Primary text: Asymptotic Statistics, A. W. van der Vaart, 1998 Edition, Cambridge University Press, ISBN: 0-521-49603-9 Texts used as references: Approximation Theorems of Mathematical Statistics, 1980 by R.J. Ser ing; Linear Statistical Inference and its Applications, 1973 by C.R. Rao. These two texts are on reserve in the Science Library (3rd floor)

Grading: The final grade is based on a midterm exam and homework. The midterm will cover parts 1 and 2 in the outline; this exam is on March 5th. The midterm exam is worth 30% of your grade. Assigned homework will count for 70%.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 621. Theory of Probability II.

Section 001.

Instructor(s): Michael B Woodroofe (michaelw@umich.edu)

Prerequisites: Stat. 620. Graduate standing. (3).

Credits: (3).

Course Homepage: No Homepage Submitted.

A continuation of Statistics 620. Topics covered include: weak convergence, characteristic functions, inversion, unicity and continuity, the central limit theorem for sequences and arrays aud, extensions to higher dimensions. Also: the renewal theorem, conditional probability and expectation, regular conditional distributions, stationary sequences aud the bergodic theorem, martingales, and the optimal stopping theorem. The course will also cover: the Poisson process, Brownian motion, the strong Markov property and the invariance principle.

TEXTBOOK:
Probability and Measure
Patrick Billingsly
3rd Edition
Wiley

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 700. Directed Reading.

Prerequisites: Graduate standing. (1-6). (INDEPENDENT).

Credits: (1-6).

Course Homepage: No Homepage Submitted.

No Description Provided

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STATS 711. Special Topics.

Section 001 Topic?

Instructor(s): Susan A Murphy (samurphy@umich.edu)

Prerequisites: Graduate standing and permission of instructor. (3).

Credits: (3).

Course Homepage: No Homepage Submitted.

No Description Provided

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STATS 711. Special Topics.

Section 002 Topic?

Prerequisites: Graduate standing and permission of instructor. (3).

Credits: (3).

Course Homepage: No Homepage Submitted.

No Description Provided

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STATS 809. Seminar in Applied Statistics II.

Section 001.

Instructor(s): Kerby A Shedden (kshedden@umich.edu)

Prerequisites: Graduate standing. (1).

Credits: (1).

Course Homepage: No Homepage Submitted.

No Description Provided

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STATS 811. Literature Proseminar II.

Section 001.

Instructor(s): Vijay Nair (vnn@umich.edu)

Prerequisites: Graduate standing and permission of instructor. (2).

Credits: (2).

Course Homepage: No Homepage Submitted.

This course is designed to acquaint students with classical papers in statistics and probability, to encourage them in critical independent reading, and to permit them to gain pedagogical experience during the course of their graduate training. NO TEXT

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STATS 818. Seminar in Mathematical Statistics I.

Section 001 Topic?

Instructor(s): George Michailidis (gmichail@umich.edu)

Prerequisites: Graduate standing. (1).

Credits: (1).

Course Homepage: No Homepage Submitted.

No Description Provided

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STATS 818. Seminar in Mathematical Statistics I.

Section 002 Topic?

Instructor(s): George Michailidis (gmichail@umich.edu)

Prerequisites: Graduate standing. (1).

Credits: (1).

Course Homepage: No Homepage Submitted.

No Description Provided

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STATS 990. Dissertation/Precandidate.

Prerequisites: Election for dissertation work by doctoral student not yet admitted as a Candidate. Graduate standing. (1-8). (INDEPENDENT). May be repeated for credit.

Credits: (1-8; 1-4 in the half-term).

Course Homepage: No Homepage Submitted.

Election for dissertation work by doctoral student not yet admitted as a Candidate.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: "5, Permission of Instructor"

STATS 993. Graduate Student Instructor Training Program.

Instructor(s): Brenda K Gunderson (bkg@umich.edu)

Prerequisites: Graduate standing. (1).

Credits: (1).

Course Homepage: No Homepage Submitted.

A seminar for all beginning graduate student instructors, consisting of a two day orientation before the term starts and periodic workshops/meetings during the Winter Academic Term. Beginning graduate student instructors are required to register for this class.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: No Data Given.

STATS 995. Dissertation/Candidate.

Prerequisites: Graduate School authorization for admission as a doctoral Candidate. Graduate standing. (8). (INDEPENDENT). May be repeated for credit.

Credits: (8; 4 in the half-term).

Course Homepage: No Homepage Submitted.

Graduate School authorization for admission as a doctoral Candidate. N.B. The defense of the dissertation (the final oral examination) must be held under a full term Candidacy enrollment period.

Check Times, Location, and Availability Cost: No Data Given. Waitlist Code: "5, Permission of Instructor"


Undergraduate Course Listings for STATS.


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