Note: You must establish a session on wolverineacccess.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 (Division 489)
This page was created at 8:06 AM on Fri, Oct 20, 2000.
Fall Term, 2000 (September 6 – December 22)
Open courses in Statistics
Wolverine Access Subject listing for STATS
Take me to the Fall Term '00 Time Schedule for Statistics.
To see what graduate courses have been added to or changed in Statistics this week go to What's New This Week.
Stat. 402. Introduction to Statistics and Data Analysis.
There will be Two (2) Evening Exams for Statistics 402, to be given on DATES TBA, from 68 p.m.
Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Econ. 404 or 405, or Stat. 265, 311, 405, or 412. (4).
Credits: (4).
Course Homepage: http://www.stat.lsa.umich.edu/~bkg/402F00/
In this course students are introduced to the concepts and applications of statistical methods and data analysis. Statistics 402 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 academic areas and some attention is given to statistical process control methods. The course format includes lectures (3 hours per week) and a laboratory (l.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 analysiscomputer package. Course evaluation is based on a combination of two examinations, a final examination, weekly homework, and lab participation.
Stat. 405/Econ. 405. Introduction to Statistics.
Section 001.
Prerequisites & Distribution: 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.
Stat. 406. Introduction to Statistical Computing.
Prerequisites & Distribution: One of Stat. 402, 405, 412, or 425. (4).Graduate credit for students outside the Stat. department.
Credits: (4).
Course Homepage: http://www.stat.lsa.umich.edu/~kshedden/Courses/Stat406/index.html
The goals of this course are:
 To provide an overview of applied statistics and modern methods of data analysis.
 To survey some of the challenging computational problems that arise in the natural
sciences, social sciences, and engineering, and to demonstrate how statistical methods
can sometimes be succesfully applied to these problems.
 To provide the student with handson experience in carrying out data analysis on a
computer and in implementing simple algorithms in a lowlevel language.
Coursework:
Weekly homework assignments will be given as we work through the topics listed below.
The assignments will involve computer work, mathematical derivation, and written
exposition of the results. There will be no exams.
Stat. 412. Introduction to Probability and Statistics.
Section 001.
Prerequisites & Distribution: 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, or 405. One credit granted to those who have completed Stat. 250 or 402. (3).
Credits: (3).
Course Homepage: No Homepage Submitted.
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.
Stat. 413. The General Linear Model and Its Applications.
Instructor(s): Alexandra Kapatou
Prerequisites & Distribution: Stat. 402 and Math. 217; concurrent enrollment in Stat. 425. Students who have not taken Math. 217 should elect Stat. 403. Two credits granted to those who have completed Stat. 403. (4).Graduate credit for students outside the Stat. department.
Credits: (4).
Course Homepage: No Homepage Submitted.
This course will introduce students to the general linear model and its assumptions, and will cover topics such as the geometry of the model projections, least squares estimation, residuals, normal distribution theory results, inference on parameters, diagnostic tools, and applications in analysis of variance, design, and the series. Three hours of lecture and 1.5 hours of lab per week. Regular homework and a final exam.
Stat. 425/Math. 425. Introduction to Probability.
Prerequisites & Distribution: Math. 215, 255, or 285. (3).
Credits: (3).
Course Homepage: No Homepage Submitted.
See Mathematics 425..
Stat. 425/Math. 425. Introduction to Probability.
Prerequisites & Distribution: 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.
Stat. 426. Introduction to Mathematical Statistics.
Section 001.
Instructor(s): Julie Horrocks (jhorrock@umich.edu), jhorrock
Prerequisites & Distribution: Stat. 425. (3).
Credits: (3).
Course Homepage: https://coursetools.ummu.umich.edu/2000/fall/lsa/stats/426/001.nsf
We will study the theory that underlies the basic components of statistical practice:
estimators, confidence intervals, and tests of hypotheses. The course begins with a
brief review of probability, and ends with a study of estimation and testing in
regression and analysis of variance.
The sequence of Statistics 425/426 serves as a prerequisite for more advanced Statistics courses. Regular homework and a final exam.
Stat. 466/IOE 466/Manufacturing 466. Statistical Quality Control.
Section 001.
Instructor(s): Jianming Shi
Prerequisites & Distribution: Stat. 265 and Stat 403 or IOE 366. (4).CAEN lab access fee required for nonEngineering students.
Credits: (4).
Lab Fee: CAEN lab access fee required for nonEngineering students.
Course Homepage: http://www.engin.umich.edu/class/ioe466/
Quality improvement philosophies; Modeling process quality, Statistical process control, Control charts for variables and attributes, CUSUM and EWMA, Short production runs, Multivariate quality control, Auto correlation, Engineering process control, Economic design of charts, Fill control, Precontrol, Adaptive schemes, Process capability, Specifications and tolerances, Gage Capability studies, Acceptance Sampling by attributes and variables, International quality standards.
Text:
 Introduction to Statistical Quality Control, 3rd edition, D.C. Montgomery, Wiley & Sons, 1996.
 Lecture Notes Coursepack, Ulrich Bookstore (549 East University 6623201)
Grading:
 Homework (as assigned) 30%
(15% for GM/CPD student)
 Exam 1 (Oct. 26th, Thursday) 35%
 Exam 2 (Dec. 12th, Tuesday) 35%
 Project (GM/CPD Student only) 15%
Stat. 470. Experimental Design.
Instructor(s): Jeff Wu
Prerequisites & Distribution: Stat. 402. (4).
Credits: (4).
Course Homepage: http://www.stat.lsa.umich.edu/~dhkim/470.html
This course will introduce students to basic principles in classical experimental design, including randomization, replication, confounding, interaction, covariates, use of the general linear model. Students will be introduced to the following designs: completely randomized, randomized blocks, Latin squares, incomplete blocks, factorial, split plot, Taguchi, response surface, optimal. There will be regular assignments and a final exam. Class format is 3 hours of lecture and 1.5 hours of laboratory per week.
Stat. 500. Applied Statistics I.
Section 001.
Prerequisites & Distribution: Math. 417, and Stat. 402 or 426. (3).
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~faraway/stat500/
Course outline:
Linear Models: Definition, fitting, inference, interpretation of
results, meaning of regression coefficients, identifiablity, lack of
fit, multicollinearity, ridge regression, principal components
regression, partial least squares, regression splines, GaussMarkov theorem, variable selection, diagnostics, transformations, influential
observations, robust procedures, ANOVA and analysis of covariance,.
Randomised block, factorial designs.
Computing:
The software I will be using for the course is R. R is very similar
to S+, the software I have used for this course in the past. R is
free with Windows and Unix versions. You can download your own copy
and use it wherever you find convenient. For details, see the
resources section of the course web site
(http://www.stat.lsa.umich.edu/~faraway/stat500/)
Textbook:
You may
download the textbook. No other text is required.
The main statistical software is described in
Modern Applied
Statistics with SPLUS by W. Venables and B. Ripley (3nd Edition).
This book will also be useful for Statistics 501.
You are not required to buy this text as you may find information
about SPlus and R on the web.
Assessment:
There will be an inclass, open book exam midterm worth 30% in late
October. Weekly assignments will be worth 30% and a final open book
exam worth 40%.
Prerequisites:
Knowledge of matrix algebra. Knowledge of the
material in Stat425/426 (probability and mathematical
statistics). Computing will be required, but
no specific prior experience is necessary.
Stat. 503. Applied Multivariate Analysis.
Section 001.
Instructor(s): George Michailidis
Prerequisites & Distribution: Stat. 500. (3).
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~gmichail/stat503F00/
Topics in applied multivariate analysis including Hotelling's T^{2} multivariate ANOVA, discriminant functions, factor analysis, principal components, canonical correlations, and cluster analysis. Selected topics from: maximum likelihood and Bayesian methods, robust estimation and survey sampling. Applications and data analysis using a computer will be stressed.
Stat. 504. Seminar on Statistical Consulting.
Section 001.
Instructor(s): Thomas Ryan
Prerequisites & Distribution: Stat. 403 or 500. (3).May be repeated for a total of eight credits.
Credits: (3).
Course Homepage: No Homepage Submitted.
Applications of statistics to problems in engineering, physical and social sciences; students will be expected to analyze data and write reports.
Stat. 505/Econ. 673. Econometric Analysis.
Section 001.
Prerequisites & Distribution: Permission of instructor. (3).
Credits: (3).
Course Homepage: No Homepage Submitted.
This course is designed for firstyear graduate students in economics, business, and related subjects. It involves a fairly rigorous development of statistical reasoning and methods relating to hypothesis testing, estimation, and regression analysis. Students are supposed to have had a course in calculus and in introductory statistics. Knowledge of matrix algebra is required. Evaluation of students is based on midterm and final examinations and weekly assignments. Students taking this course are expected to take Economics 674 – Econometric Analysis II in the following term.
Stat. 510. Mathematical Statistics I.
Section 001.
Instructor(s): Robert Keener
Prerequisites & Distribution: Math. 450 or 451, and a course in probability or statistics. (3).
Credits: (3).
Course Homepage: No Homepage Submitted.
Review of probability, exponential families, sufficiency, completeness, Basu's Theorem, unbiased estimation, curved exponential families, information inequalities, conditional probability, Bayesian estimation, large sample theory.
Stat. 525/Math. 525. Probability Theory.
Prerequisites & Distribution: 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.
Stat. 550/SMS 576 (Business Administration)/IOE 560. Bayesian Decision Analysis.
Section 001.
Prerequisites & Distribution: Stat. 425. (3).CAEN lab access fee required for nonEngineering students.
Credits: (3).
Lab Fee: CAEN lab access fee required for nonEngineering students.
Course Homepage: https://coursetools.ummu.umich.edu/2000/fall/bus/sms/603/001.nsf
Axiomatic foundations for, and assessment of, probability and utility; formulation of decision problems; risk functions, admissibility; likelihood functions and the likelihood principle; natural conjugate a priori distributions; Bayesian regresion analysis and hypothesis testing; hierarchical models; credible intervals; numerical analysis; applications to decisionmaking.
Stat. 575/Econ. 775. Econometric Theory I.
Section 001.
Prerequisites & Distribution: Math. 417 and 425 or Econ. 653, 654, 673, and 674. (3).
Credits: (3).
Course Homepage: No Homepage Submitted.
The purpose of this course is to develop the results of asymptotic distribution theory needed for statistical inference in econometrics and to use these results to derive the properties of various estimators and test procedures used in econometrics. The course is a prerequisite for Statistics 576 (Econometric Theory II).
Stat. 600. Advanced Topics in Applied Statistics I and II.
Section 001.
Prerequisites & Distribution: Stat. 501 and Graduate standing. (3).
Credits: (3).
Course Homepage: No Homepage Submitted.
No Description Provided
Check Times, Location, and Availability
Stat. 610. Mathematical Statistics I.
Section 001.
Instructor(s): George Michailidis
Prerequisites & Distribution: Math. 601 and 625. Graduate standing. (3).
Credits: (3).
Course Homepage: No Homepage Submitted.
Selected topics in mathematical statistics; assumes knowledge of measure theory.
Stat. 620. Theory of Probability I.
Section 001.
Instructor(s): Michael Woodroofe
Prerequisites & Distribution: Math. 451 or the equivalent. Graduate standing. (3).
Credits: (3).
Course Homepage: No Homepage Submitted.
Basics of probability at an advanced level. Specific topics include: discrete probability spaces, the weak law of large numbers, the de MoivreLaplace theorems, classes of sets, algebras, measures, extension of measures, countable additivity and Lebesgue and product measures. Also: measurable functions, random variables, conditional probability, independence, the BorelCantelli lemmas and the zeroone law. The course will additionally cover: integration, convergence theorems, inequalities, Fubini's theorem, the RadonNikodym theorem, distribution functions, expectations, and the strong law of large numbers.
Stat. 626/Math. 626. Probability and Random Processes II.
Section 001.
Prerequisites & Distribution: Stat. 625. Graduate standing. (3).
Credits: (3).
Course Homepage: http://www.math.lsa.umich.edu/~conlon/math626.html
See Mathematics 626.001.
Stat. 642/Biostat. 851 (Public Health). Linear Statistical Models.
Section 001.
Instructor(s): Anant Kshirsagar
Prerequisites & Distribution: Math. 417 and either Stat. 511 or Biostat. 602. Graduate standing. (3).
Credits: (3).
Course Homepage: No Homepage Submitted.
General linear model, estimability, GaussMarkov theorem, general linear hypothesis, analysis of variance and covariance, multiple comparisons, variance components.
Stat. 700. Directed Reading.
Prerequisites & Distribution: Graduate standing. (16). (INDEPENDENT).
Credits: (16).
Course Homepage: No Homepage Submitted.
No Description Provided
Check Times, Location, and Availability
Stat. 710. Special Topics.
Section 001 – Modeling and Computational Issues in Bioinformatics
Prerequisites & Distribution: Graduate standing and permission of instructor. (3).
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~kshedden/Courses/Stat710/
In the first part of the course, the instructors will give a broad overview of the relevant
aspects of cellular biology, machine learning, and the engineering aspects of highthroughput
experimentation. In the second part of the course, invited guests will give talks on more
specialized aspects of the subject. Students will be required to
 carry out a computational
data analysis of a genomics or proteomics data set and prepare a written report of the results, and
 give a presentation in class of a paper related to the topic of the course.
Stat. 808. Seminar in Applied Statistics I.
Section 001.
Prerequisites & Distribution: Graduate standing. (1).
Credits: (1).
Course Homepage: No Homepage Submitted.
No Description Provided
Check Times, Location, and Availability
Stat. 810. Literature Proseminar I.
Section 001.
Instructor(s): Michael Woodroofe
Prerequisites & Distribution: 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 mathematical and applied statistics and probability theory, to encourage them in critical independent reading, and to permit them to gain pedagogical experience during the course of their graduate training.
Stat. 990. Dissertation/Precandidate.
Prerequisites & Distribution: Election for dissertation work by doctoral student not yet admitted as a Candidate. Graduate standing. (18). (INDEPENDENT). May be repeated for credit.
Credits: (18; 14 in the halfterm).
Course Homepage: No Homepage Submitted.
Election for dissertation work by doctoral student not yet admitted as a Candidate.
Stat. 993. Graduate Student Instructor Training Program.
Prerequisites & Distribution: 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 Fall Term. Beginning graduate student instructors are required to register for this class.
Stat. 995. Dissertation/Candidate.
Prerequisites & Distribution: Graduate School authorization for admission as a doctoral Candidate. Graduate standing. (8). (INDEPENDENT). May be repeated for credit.
Credits: (8; 4 in the halfterm).
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.
This page was created at 8:06 AM on Fri, Oct 20, 2000.
University of Michigan  College of LS&A  LS&A Research and Graduate Education  Rackham Bulletin Index  Rackham School of Graduate Studies
This page maintained by LS&A Academic Information and Publications, 1228 Angell Hall
Copyright © 2000 The Regents of the University of Michigan,
Ann Arbor, MI 48109 USA +1 734 7641817
Trademarks of the University of Michigan may not be electronically or otherwise altered or separated from this document or used for any nonUniversity purpose.
