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Winter Academic Term, 2003 (January 6  April 25)
STATS 400. Applied Statistical Methods.
Section 001.
Instructor(s):
Elizaveta Levina
Prerequisites: High School Algebra. No credit granted to those who have completed or are enrolled in ECON 404 or 405, or STATS 250, 265, 350, 405, or 412. (4). May not be repeated for credit.
Credits: (4).
Course Homepage: http://www.stat.lsa.umich.edu/~elevina/stat400/index.html
Statistics and the scientific method; observational study versus designed experiment;
visualization; introduction to probability; statistical inference; confidence intervals; onesample tests of hypothesis; twosample problems; analysis of variance (ANOVA); blocked designs; tests for association and independence (chisquare tests); regression and correlation; and nonparametric tests. Course format includes lectures (3 hours per week) and a laboratory (1.5 hours per week).
STATS 401(403). Applied Statistical Methods II.
Section 001.
Prerequisites: STATS 350 or 400. No credit granted to those who have completed or are enrolled in STAT 413. (4). May not be repeated for credit.
Credits: (4).
Course Homepage: http://www.stat.lsa.umich.edu/~kshedden/Courses/Stat401/index.html
An intermediate course in applied statistics, covering a range of topics in modeling and analysis of data including: review of simple linear regression, twosample problems, oneway analysis of variance; multiple linear regression, diagnostics and model selection; twoway analysis of variance, multiple comparisons, and other
selected topics. Three hours of lecture supplemented by one and onehalf hours of laboratory.
STATS 405 / ECON 405. Introduction to Statistics.
Section 001.
Prerequisites: MATH 116. 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 IOE 265, STATS 265, 400 or 412. Students with credit for ECON 404 can only elect STATS 405 for 2 credits and must have permission of instructor. (4). May not be repeated for credit.
Credits: (4).
Course Homepage: No homepage submitted.
The purpose of this course is to provide students with an understanding of the
principles of statistical inference. Topics include probability, experimental and
theoretical derivation of sampling distributions, hypothesis testing, estimation, and simple regression. (Students are advised to elect the sequel, ECON 406.)
TEXTBOOK:
Statistics: Theory and Methods,
Berry/Lindgren,
2nd Edition.
STATS 408. Statistical Principles for Problem Solving: A Systems Approach.
Section 001.
Prerequisites: High school algebra. No credit granted to those who have completed or are enrolled in STATS 170. (4). May not be repeated for credit.
Credits: (4).
Course Homepage: http://coursetools.ummu.umich.edu/2003/winter/stats/408/001.nsf
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.
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: a journal (max 20 points); test score (max 30 points); and discussion section leader bonus (max 5 additional points).
TEXTBOOKS:
 Theory of Constraints,
E. Goldratt,
Northriver Press;
 The Goal,
E. Goldratt,
Northriver Press;
 The Fifth Discipline,
Peter M. Senge,
Doubleday Currency; and
 The New Economics for Industry Government Education,
W. Edwards Deming,
2nd Edition,
MIT.
STATS 412. Introduction to Probability and Statistics.
Section 001.
Instructor(s):
Elizaveta Levina
Prerequisites: Prior or concurrent enrollment in MATH 215 and EECS 183. No credit granted to those who have completed or are enrolled in ECON 405, STATS 265, 400, or 405, or IOE 265. One credit granted to those who have completed or are enrolled in STATS 350. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~elevina/stat412/index.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.
STATS 425 / MATH 425. Introduction to Probability.
Instructor(s):
Prerequisites: MATH 215, 255, or 285. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
Basic concepts of probability; expectation, variance, covariance; distribution functions; and bivariate, marginal, and conditional distributions.
STATS 425 / MATH 425. Introduction to Probability.
Section 001.
Prerequisites: MATH 215, 255, or 285. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://wwwpersonal.umich.edu/~jrs/math425.html
See Mathematics 425.001.
STATS 425 / MATH 425. Introduction to Probability.
Section 002.
Prerequisites: MATH 215, 255, or 285. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://www.math.lsa.umich.edu/~sadovska/425/425.html
See Mathematics 425.002.
STATS 425 / MATH 425. Introduction to Probability.
Section 003.
Prerequisites: MATH 215, 255, or 285. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://www.math.lsa.umich.edu/~kalinin/Teach/425/425index.html
See Mathematics 425.003.
STATS 425 / MATH 425. Introduction to Probability.
Section 007 – Section 007 ONLY satisfies the upperlevel writing requirement.
Instructor(s):
Burns Jr
Prerequisites: MATH 215, 255, or 285. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
See Mathematics 425.007.
STATS 426. Introduction to Theoretical Statistics.
Section 001.
Prerequisites: STATS 425. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~moulib/stat426win03.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 STATS 425/426 serves as a prerequisite for more advanced Statistics courses. Regular homework and a final exam.
Topic covered include:
 Random Variables
 Joint Distributions
 Induced Distributions
 Expectation
 The Law of Large Numbers
 The Central Limit Theorem
 Simulation
 Populations and Samples
 The Chisquared, t, and F Distributions
 Estimation: The Method of Moments
 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
 ChiSquared 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

STATS 466 / IOE 466 / MFG 466. Statistical Quality Control.
Section 001.
Instructor(s):
Prerequisites: STATS 265 and 401 or IOE 366. (4). CAEN lab access fee required for nonEngineering students. May not be repeated for credit.
Credits: (4).
Lab Fee: CAEN lab access fee required for nonEngineering students.
Course Homepage: http://coursetools.ummu.umich.edu/2003/winter/ioe/466/001.nsf
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.
STATS 480. Survey Sampling Techniques.
Section 001.
Prerequisites: STATS 350. (4). Graduate credit for students outside the Stat. department.May not be repeated for credit.
Credits: (4).
Course Homepage: No homepage submitted.
Introduces students to basic ideas in survey sampling, moving from motivating examples to abstraction to populations, variables, parameters, samples and sample design, statistics, sampling distributions, HorvitzThompson estimators, basic sample design (simple random, cluster, systematics, multiple stage), various errors and biases, special topics. There will be weekly assignments and a final exam. Class format is three hours of lecture and one hour of laboratory per week.
STATS 501. Applied Statistics II.
Section 001.
Prerequisites: STATS 500. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~faraway/stat501/
Generalized Linear Models, Analysis of binary and categorical data, Loglinear models, Random and mixed effects models, Smoothing and nonparametric 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.
STATS 525 / MATH 525. Probability Theory.
Instructor(s):
Sadovskaya
Prerequisites: MATH 450 or 451. Students with credit for STATS 425/MATH 425 can elect STATS 525/MATH 525 for only one credit. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
See Mathematics 525.001.
STATS 526 / MATH 526. Discrete State Stochastic Processes.
Section 001.
Instructor(s):
Prerequisites: STATS 525 or EECS 501. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
See Mathematics 526.001.
STATS 531 / ECON 677. Analysis of Time Series.
Section 001.
Instructor(s):
Edward L Ionides
Prerequisites: STATS 426. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~ionides/531/
Decomposition of series; trends and regression as a special case of time series; cyclic components; smoothing techniques; the variate difference method; representations including spectrogram, periodogram, etc.; stochastic difference equations, autoregressive schemes, moving averages; large sample inference and prediction; covariance structure and spectral densities; hypothesis testing, estimation, and applications, and other topics.
STATS 535 / IOE 562. Reliability.
Section 001.
Prerequisites: STATS 425 and 426 (or IOE 316 and 366). (3). CAEN lab access fee required for nonEngineering students. May not be repeated for credit.
Credits: (3).
Lab Fee: CAEN lab access fee required for nonEngineering students.
Course Homepage: http://www.stat.lsa.umich.edu/~vnn/ioe562/index.html
This course will cover the important reliability concepts and methodology that arise in modeling, assessing and improving product reliability and in analyzing field and warranty data. Topics will be selected from the following:
 Basic reliability concepts;
 Common parametric models for component reliability;
 Censoring schemes;
 Analysis of timetofailure data;
 Accelerated testing for reliability assessment;
 Modeling and analyzing repairable systems reliability;
 Analysis of warranty and fieldfailure data;
 Maintenance policies and availability;
 Reliability improvement through experimentation.
STATS 547 / MATH 547 / BIOINF 547. Probabilistic Modeling in Bioinformatics.
Section 001.
Instructor(s):
Burns
Prerequisites: STATS 425 or MCDB 427 or BIOLCHEM 415; basic programming skills desirable. Graduate standing and permission of instructor. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
Prerequisites: These are flexible, due to the diverse backgrounds of the intended audience. Basic probability (level of Math/Stat 425), molecular biology (level of Bio 427), or biochemistry (level of Chem/BioChem 451), or basic programming skills desirable; or permission of instructor.
Content: Probabilistic models of proteins and nucleic acids. Analysis of DNA/RNA and protein sequence data. Algorithms for sequence alignment, statistical analysis of similarity scores, hidden Markov models, neural networks, training, gene finding, protein family profiles, multiple sequence alignment, sequence comparison and structure prediction. Analysis of expression array data.
STATS 548 / MATH 548. Computations in Probabilistic Modeling in Bioinformatics.
Section 001.
Instructor(s):
Burns
Prerequisites: STATS 425 or MCDB 427 or BIOLCHEM 415; basic programming skills desirable. Graduate standing and permission of instructor. (1). May not be repeated for credit.
Credits: (1).
Course Homepage: No homepage submitted.
See Mathematics 548.001.
STATS 550 / IOE 560 / SMS 603. Bayesian Decision Analysis.
Section 001.
Instructor(s):
Stephen M Pollock
Prerequisites: STATS 425. (3). CAEN lab access fee required for nonEngineering students. May not be repeated for credit.
Credits: (3).
Lab Fee: CAEN lab access fee required for nonEngineering students.
Course Homepage: http://coursetools.ummu.umich.edu/2003/winter/ioe/560/001.nsf
Topics:
 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.
STATS 570 / IOE 570. Experimental Design.
Section 001.
Prerequisites: STATS 500 or background in regression. Graduate standing. (3). CAEN lab access fee required for nonEngineering students. May not be repeated for credit.
Credits: (3).
Lab Fee: CAEN lab access fee required for nonEngineering students.
Course Homepage: No homepage submitted.
Basic topics and ideas in the design of experiments:
 randomization and randomization tests;
 the validity and analysis of randomized experiments;
 randomized blocks;
 Latin and GraecoLatin squares;
 plot techniques;
 factorial experiments;
 the use of confounding and response surface methodology;
 weighing designs, lattice and incomplete block and partially balanced incomplete block designs.
STATS 576 / ECON 679. Econometric Theory II.
Section 001.
Prerequisites: STATS 575. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://www.econ.lsa.umich.edu/~ssakata/courses/info/econ679/syllabus.html
See Economics 679.001.
STATS 580 / SOC 717 / BIOSTAT 617. Methods and Theory of Sample Design.
Section 001.
Prerequisites: Three or more courses in statistics and preferably a course in methods of survey sampling. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://coursetools.ummu.umich.edu/2003/winter/biostat/617/001.nsf
Theory underlying sample designs and estimation procedures commonly used in survey
practice. Simple random sampling, stratification systematic sampling, cluster sampling, multistage sampling, sampling with probability proportional to size, replicated sampling, multiphase sampling. Poststratification, ratio, regression and difference estimation. Variance estimation with complex sample designs: Taylor series method, repeated replications, jackknife repeated replications. Nonresponse
weighting adjustments and imputation.
STATS 606. Statistical Computing.
Section 001.
Prerequisites: Calculus, Linear Algebra, some knowledge of Probability and Statistics. Graduate standing. (3). May not be repeated for credit.
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 primarily will focus on the design and implementation of algorithms.
STATS 611. Mathematical Statistics II.
Section 001.
Prerequisites: STAT 610. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~moulib/stat611.html
More on the theory of estimation including: minimax, Bayes and JamesStein estimators. The theory of hypothesis testing including: tests significance levels, power, the NeymanPearson lemma, uniformly most powerful unbiased tests, monotone likelihood ratios, locally best tests, similar tests, likelihood ratio tests and the associated large sample theory, sequential tests, goodness of fit tests, and tests in contingency tables. Other topics include: confidence regions, introduction to
the general linear model, and nonparametric methods.
STATS 621. Theory of Probability II.
Section 001.
Prerequisites: STATS 620. Graduate standing. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
A continuation of STATS 620. Topics covered include:
 weak convergence, characteristic functions, inversion, unicity and continuity, the central limit theorem for sequences and arrays and extensions to higher dimensions.
 the renewal theorem, conditional probability and expectation, regular conditional distributions, stationary sequences and the bergodic theorem, martingales, and the optimal stopping theorem.
 the Poisson process, Brownian motion, the strong Markov property and the invariance principle.
STATS 640 / BIOSTAT 890. Multivariate Statistical Models.
Section 001.
Instructor(s):
Anant M Kshirsagar
Prerequisites: MATH 417 and either STATS 511 or BIOSTAT 602; Graduate standing and permission of instructor. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
Wishart distribution, multivariate linear models, multivariate regression, Hotelling's Tsquare
and its applications, discriminant analysis, canonical correlations, principal components analysis, growth curves.
STATS 701. Special Topics in Applied Statistics II.
Section 001 – Dark Matter in the Universe: An Interdisciplinary Seminar. Meets with Statistics 701.001 and Philosophy 596.
Prerequisites: STATS 501 and graduate standing. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://wwwpersonal.umich.edu/~jjoyce/dark.html
See Astronomy 699.009.
STATS 750(700). Directed Reading.
Instructor(s):
Prerequisites: Graduate standing and permission of instructor. (16). (INDEPENDENT). May be elected for a maximum of 5 credits. May be elected more than once in the same term.
Credits: (16).
Course Homepage: No homepage submitted.
Designed for individual
students who have an interest in a specific topic (usually that has
stemmed from a previous course). An individual instructor must agree to
direct such a reading, and the requirements are specified when approval is
granted.
STATS 809. Seminar in Applied Statistics II.
Section 001 – Topic?
Prerequisites: Graduate standing. (1). May not be repeated for credit.
Credits: (1).
Course Homepage: No homepage submitted.
No Description Provided. Contact the Department.
STATS 811. Literature Proseminar II.
Section 001.
Prerequisites: Graduate standing and permission of instructor. (2). May not be repeated for credit.
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.
STATS 817 / PSYCH 817 / SOC 810 / EDUC 817. Interdisciplinary Seminar in Quantitative Social Science Methodology.
Section 001 – MEETS EVERY OTHER TUESDAY, BEGINNING JANUARY 8, 2002. [Drop/Add deadline=January 24].
Instructor(s):
Prerequisites: Graduate standing, and graduatelevel course in STATS at the level of STAT 500 and 501. (1). This course has a grading basis of "S" or "U." May be repeated for credit for a maximum of 6 credits.
Mini/Short course
Credits: (1).
Course Homepage: No homepage submitted.
This seminar will meet to
consider methodological issues that arise in research in the social
sciences. Themes for each meeting will arise from ongoing research
projects at the University of Michigan. Visiting researchers will provide
a brief account of their aims and data before defining the methodological
challenge for which they desire discussion.
STATS 990. Dissertation/Precandidate.
Instructor(s):
Prerequisites: 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.
STATS 993. Graduate Student Instructor Training Program.
Section 001.
Prerequisites: Graduate standing. (1). May not be repeated for credit.
Credits: (1).
Course Homepage: No homepage submitted.
A seminar for all beginning graduate student instructors, consisting of a twoday orientation before the term starts and periodic workshops/meetings during the term. Beginning graduate student instructors are required to register for this course.
STATS 995. Dissertation/Candidate.
Instructor(s):
Prerequisites: 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 8:42 AM on Thu, Feb 6, 2003.
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