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Open courses in Statistics (*Not realtime Information. Review the "Data current as of: " statement at the bottom of hyperlinked page)
Wolverine Access Subject listing for STATS
Winter Academic Term '02 Time Schedule for Statistics.
Section 001.
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, 402, 405, or 412. (4).
Credits: (4).
Course Homepage: http://www.stat.lsa.umich.edu/~dbingham/Stat400/
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. (4).
Credits: (4).
Course Homepage: http://www.stat.lsa.umich.edu/~kshedden/Courses/Stat403/
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 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 Stats. 265, 311, 400 or 412. Students with credit for Econ. 404 can only elect Stats. 405 for 2 credits and must have permission of instructor. (4).
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, Economics 406.)
TEXTBOOK:
Statistics: Theory and Methods,
Berry/Lindgren,
2nd Edition,
ISBN: 0534504795,
Brooks/Cole.
STATS 408. Statistical Principles for Problem Solving: A Systems Approach.
Section 001 – Meets with Statistics 170.001.
Prerequisites: High school algebra. No credit granted to those who have completed or are enrolled in Statistics 170. (4).
Credits: (4).
Course Homepage: http://coursetools.ummu.umich.edu/2002/winter/stats/170/001.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.
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 Stats. 265, 311, 350, 400, or 405. One credit granted to those who have completed Stats. 350 or 402. (3).
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~moulib/stat412winter.html
An introduction to probability theory; statistical models, especially sampling models; estimation and confidence intervals; testing statistical hypotheses; and important applications, including the analysis of variance and regression.
STATS 425 / MATH 425. Introduction to Probability.
Section 001, 003, 007.
Instructor(s):
Prerequisites: Math. 215, 255, or 285. (3).
Credits: (3).
Course Homepage: No homepage submitted.
See Mathematics 425.001.
STATS 425 / MATH 425. Introduction to Probability.
Section 002.
Prerequisites: Math. 215, 255, or 285. (3).
Credits: (3).
Course Homepage: http://www.math.lsa.umich.edu/~barvinok/m425.html
See Mathematics 425.002.
STATS 425 / MATH 425. Introduction to Probability.
Section 004, 005, 006.
Instructor(s):
Prerequisites: Math. 215, 255, or 285. (3).
Credits: (3).
Course Homepage: No homepage submitted.
Basic concepts of probability; expectation, variance, covariance; distribution functions; and bivariate, marginal, and conditional distributions.
STATS 426. Introduction to Theoretical Statistics.
Section 001.
Prerequisites: Stats. 425. (3).
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~moulib/stat426winter.html
An introduction to theoretical statistics for students with a background in probability. Probability models for experimental and observational data, normal sampling theory, likelihoodbased and Bayesian approaches to point estimation, confidence intervals, tests of hypotheses, and an introduction to regression and the analysis of variance.
Text: Mathematical Statistics and Data Analysis by John Rice
STATS 466 / IOE 466 / MFG 466. Statistical Quality Control.
Section 001.
Instructor(s):
Prerequisites: Stats. 265 and Stats. 401 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: No homepage submitted.
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 (or 402). (4). Graduate credit for students outside the Stat. department.
Credits: (4).
Course Homepage: http://www.stat.lsa.umich.edu/~jeffwu/stat480.html
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.
Instructor(s):
Prerequisites: Stats. 500. (3).
Credits: (3).
Course Homepage: No homepage submitted.
Generalized linear models including logistics regression, Poisson regression, contingency tables; random effects and repeated measures; modern regression techniques; regression and classification trees; neural networks.
STATS 525 / MATH 525. Probability Theory.
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: http://www.math.lsa.umich.edu/~barvinok/m525.html
See Mathematics 525.001.
STATS 526 / MATH 526. Discrete State Stochastic Processes.
Instructor(s):
Prerequisites: Stats. 525 or EECS 501. (3).
Credits: (3).
Course Homepage: No homepage submitted.
No Description Provided
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STATS 526 / MATH 526. Discrete State Stochastic Processes.
Section 001.
Instructor(s):
Prerequisites: Stats. 525 or EECS 501. (3).
Credits: (3).
Course Homepage: No homepage submitted.
Review of discrete distributions; generating functions; compound distributions, renewal theorem; modeling of systems as Markov chains; first properties; ChapmanKolmogorov equations; return and first passage times; classification of states and periodicity; absorption probabilities and the forward equation; stationary distributions and the backward equation; ergodicity; limit properties; application
to branching and queueing processes; examples from engineering, biological, and social sciences; Markov chains in continuous time; embedded chains; the M/G/1 queue; Markovian decision processes; application to inventory problems; other topics at instructor's discretion.
STATS 531 / ECON 677. Analysis of Time Series.
Section 001.
Prerequisites: Stats. 426. (3).
Credits: (3).
Course Homepage: http://coursetools.ummu.umich.edu/2002/winter/stats/531/001.nsf
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.
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. Probabilistic Modeling in Bioinformatics.
Section 001.
Prerequisites: Stat. 425 or Biol. 427 or Biol. Chem. 415; basic programming skills desirable. Graduate standing and permission of instructor. (3).
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~kshedden/Courses/Stat547/
The focus of this course will be computational and algorithmic aspects of the analysis of
gene and protein sequence data. A special emphasis will be placed on the use of proba
bility models for the motivation and derivation of algorithms, but heuristicallymotivated
algorithms will be considered as well. The mathematical and statistical topics will include:
review of elementary probability and statistical estimation theory, multinomial and Dirich
let distributions, simulation, Markov chains, dynamic programming, hidden Markov models,
change point detection, Bayesian methods, classication, EM algorithm, and Gibbs sampling.
The biological topics will include a review of the relevant molecular biology, pairwise and
multiple aligment, identication of coding regions, splice prediction, motif searches, evolu
tionary inference, BLAST, structure prediction, TF binding site identication, and shotgun
sequencing.
The mathematical/statistical and biological material will be developed from rst principles.
However a certain level of quantitative maturity is expected. Strongly motivated students
with a background in only one of the two areas are encouraged to enroll.
The laboratory course 548 will focus on using PERL as a tool for computational biological
sequence analysis. The required work for both courses 547 and 548 will include programming
exercises. Students planning to enroll in 547 who have little or no programming background
are strongly encouraged to enroll in 548 concurrently. Students with a moderate or advanced
programming background (not necessarily in PERL) may choose not to enroll in 548.
Recommended Texts: Statistical Methods in Bioinformatics : An Introduction (G.R. Grant,
W.J. Ewens). Biological Sequence Analysis (R. Durbin, S. Eddy, A. Krogh, G. Mitchison).
STATS 548 / MATH 548. Computations in Probabilistic Modeling in Bioinformatics.
Section 001.
Instructor(s): Carlos Santos
Prerequisites: Stat. 425 or Biol. 427 or Biol. Chem. 415; basic programming skills desirable. Graduate standing and permission of instructor. (1).
Credits: (1).
Course Homepage: http://www.bioinformatics.med.umich.edu/~bioinfo548/index.html
See Mathematics .
STATS 560 / BIOSTAT 685. Introduction to Nonparametric Statistics.
Section 001.
Instructor(s):
Prerequisites: Stats. 426. (3).
Credits: (3).
Course Homepage: No homepage submitted.
Confidence intervals and tests for quantiles, tolerance regions, and coverages; estimation by U statistics and linear combination or order statistics; large sample theory for U statistics and order statistics; the sample distribution and its uses including goodnessoffit tests; rank and permutation tests for several hypotheses including a discussion of locally most powerful rank and permutation tests; and large
sample and asymptotic efficiency for selected tests.
STATS 570 / IOE 570. Experimental Design.
Section 001.
Prerequisites: Stat 500 or background in regression. Graduate standing. (3). CAEN lab access fee required for nonEngineering students.
Credits: (3).
Lab Fee: CAEN lab access fee required for nonEngineering students.
Course Homepage: http://www.stat.lsa.umich.edu/~dbingham/Stat570/index.html
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 in complete block designs.
STATS 576 / ECON 679. Econometric Theory II.
Section 001.
Prerequisites: Stats. 575. (3).
Credits: (3).
Course Homepage: http://www.econ.lsa.umich.edu/~ssakata/courses/info/econ679/syllabus.html
Generalized least squares, multivariate multiple regression, simultaneous equation
models (including problems of identification, estimation by equation and system methods, and forecasting), introduction to asymptotic theory, and estimation problems in time series models.
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).
Credits: (3).
Course Homepage: No homepage submitted.
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).
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: Math. 601 and 625. Graduate standing. (3).
Credits: (3).
Course Homepage: No homepage submitted.
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: Stat. 620. Graduate standing. (3).
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~samurphy/classes.html
A continuation of Statistics 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. Also: the renewal theorem, conditional probability and expectation, regular conditional distributions, stationary sequences and the bergodic theorem, martingales, and the optimal stopping theorem. The course also will cover: the Poisson process, Brownian motion, the strong Markov property and the invariance principle.
STATS 630. Topics in Applied Probability.
Section 001.
Instructor(s):
Prerequisites: Stat. 626. Graduate standing. (3).
Credits: (3).
Course Homepage: No homepage submitted.
Advanced topics in applied probability, such as queueing theory, inventory problems, branching processes, stochastic difference and differential equations, etc. The course will study one or two advanced topics in detail.
STATS 640 / BIOSTAT 890. Multivariate Statistical Models.
Section 001.
Instructor(s):
Prerequisites: Math. 417 and either Stat. 511 or Biostat. 602; Graduate standing and permission of instructor. (3).
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 750. Directed Reading.
Section 001.
Instructor(s):
Prerequisites: Graduate standing and permission of instructor. (16). (INDEPENDENT).
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.
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.
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 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.
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 a total of six credits.
Credits: (1).
Course Homepage: No homepage submitted.
This seminar considers methodological issues that arise in research in the social sciences. Themes arise from ongoing research projects at the UM. Visiting researchers provide a brief account of their aims and data before defining the methodological challenges for which they desire discussion.
STATS 819. Seminar in Mathematical Statistics II.
Section 001.
Instructor(s):
Prerequisites: Graduate standing. (1).
Credits: (1).
Course Homepage: No homepage submitted.
No Description Provided
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STATS 819. Seminar in Mathematical Statistics II.
Section 002.
Instructor(s):
Prerequisites: Graduate standing. (1).
Credits: (1).
Course Homepage: No homepage submitted.
No Description Provided
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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).
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.
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