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Fall Academic Term 2003 (September 2  December 19)
STATS 400. Applied Statistical Methods.
Instructor(s):
Bendek B Hansen
Prerequisites: High School Algebra. (4). May not be repeated for credit. No credit granted to those who have completed or are enrolled in ECON 404 or 405, or STATS 250, 265, 350, 405, or 412.
Credits: (4).
Course Homepage: http://coursetools.ummu.umich.edu/2003/fall/stats/400/001.nsf
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.
Prerequisites: MATH 115, and STATS 350 or 400. (4). May not be repeated for credit. No credit granted to those who have completed or are enrolled in STATS 413.
Credits: (4).
Course Homepage: No homepage submitted.
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.
STATS 405 / ECON 405. Introduction to Statistics.
Section 001.
Instructor(s):
Prerequisites: MATH 116. Juniors and seniors may elect this course concurrently with ECON 101 or 102. (4). May not be repeated for credit. 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.
Credits: (4).
Course Homepage: No homepage submitted.
See Economics 405.001.
STATS 406. Introduction to Statistical Computing.
Prerequisites: One of STATS 401, 412, or 425. (4). May not be repeated for credit. Graduate credit for students outside the Statistics department.
Credits: (4).
Course Homepage: http://www.stat.lsa.umich.edu/~kshedden/Courses/Stat406/index.html
Acquaints students with selected topics in statistical computing, including basic numerical aspects, iterative statistical methods, principles of graphical analyses, simulation and Monte Carlo methods, generation of random variables, stochastic modeling, importance sampling, and numerical and Monte Carlo integration. Three hours of lecture and 1.5 hour laboratory session each week.
STATS 412. Introduction to Probability and Statistics.
Section 001.
Prerequisites: Prior or concurrent enrollment in MATH 215 and EECS 183. (3). May not be repeated for credit. 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.
Credits: (3).
Course Homepage: No homepage submitted.
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.
Instructor(s):
Statistic faculty
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.
Instructor(s):
Mathematics faculty
Prerequisites: MATH 215, 255, or 285. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
See Mathematics 425.
STATS 425 / MATH 425. Introduction to Probability.
Section 001.
Instructor(s):
Prerequisites: MATH 215, 255, or 285. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
See Mathematics 425.001.
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: 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 007.
Prerequisites: MATH 215, 255, or 285. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://www.math.lsa.umich.edu/~daschnei/math425/Math425HomePage.htm
See Mathematics 425.007.
STATS 425 / MATH 425. Introduction to Probability.
Section 008.
Instructor(s):
Kausch
Prerequisites: MATH 215, 255, or 285. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://coursetools.ummu.umich.edu/2003/fall/math/425/008.nsf
See Mathematics 425.008.
STATS 426. Introduction to Theoretical Statistics.
Section 001.
Prerequisites: STATS 425. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
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.
Instructor(s):
Prerequisites: STATS 265 and 401 or IOE 366. (4). May not be repeated for credit. 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 466 / IOE 466 / MFG 466. Statistical Quality Control.
Section 881.
Instructor(s):
Jianjun Shi
Prerequisites: STATS 265 and 401 or IOE 366. (4). May not be repeated for credit. 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.
No Description Provided. Contact the Department.
STATS 470. Introduction to the Design of Experiments.
Instructor(s):
Prerequisites: STATS 401. (4). May not be repeated for credit.
Credits: (4).
Course Homepage: No homepage submitted.
This course will introduce students to basic principles in classical experimental design, including randomization, replication, confounding, interaction, covariates, and 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, and 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.
STATS 480. Survey Sampling Techniques.
Section 001.
Prerequisites: STATS 401. (4). May not be repeated for credit. Graduate credit for students outside the Stat. department.
Credits: (4).
Course Homepage: http://coursetools.ummu.umich.edu/2003/fall/stats/480/001.nsf
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, systematic, multiple stage), various errors and biases, special topics.
STATS 500. Applied Statistics I.
Section 001.
Instructor(s):
Ji Zhu
Prerequisites: MATH 417, and STATS 350 or 426. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://wwwpersonal.umich.edu/~jizhu/Teaching/Stat500/
Linear Models: definition, fitting, GaussMarkov theorem, inference, interpretation of results, meaning of regression coefficients, identifiability, diagnostics, influential
observations, multicollinearity, lack of fit, robust procedures, transformations, regression splines, variable selection, ridge regression, principal components regression, ANOVA
and analysis of covariance. The objective is to learn what methods are available and more importantly, when they should be applied.
Textbook:
Linear Models with R by Julian J. Faraway.
Computing:
The software we will be using for the course is R. R is free with Windows and Unix versions.
Prerequisites:
Knowledge of matrix algebra. Knowledge of basic probability and mathematical statistics (at the level of STATS 425/426).
Assessment:
There will be weekly homeworks, one midterm and one final. The weights are 30% for the homework, 30% for the midterm and 40% for the final. No late homework will be
accepted. No makeup exam.
STATS 500. Applied Statistics I.
Section 002.
Prerequisites: MATH 417, and STATS 350 or 426. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~faraway/stat500/
Linear Models: Definition, fitting, inference, interpretation of
results, meaning of regression coefficients, identifiability, 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, and 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.
STATS 504. Statistical Consulting.
Section 001.
Prerequisites: STATS 401 or 500. (3). May be repeated for credit for a maximum of 9 credits.
Credits: (3).
Course Homepage: No homepage submitted.
Applications of statistics to problems in engineering, physical sciences, and social sciences; students will be expected to analyze data and write reports.
STATS 505 / ECON 671. Econometric Analysis I.
Section 001.
Prerequisites: Permission of instructor. (3). May not be repeated for credit.
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.
STATS 525 / MATH 525. Probability Theory.
Section 001.
Instructor(s):
Charles R Doering
Prerequisites: MATH 451 (strongly recommended) or 450. STATS 425 would be helpful. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
See Mathematics 525.001.
STATS 570 / IOE 570. Experimental Design.
Section 001.
Prerequisites: STATS 500 or background in regression. Graduate standing. (3). May not be repeated for credit. CAEN lab access fee required for nonEngineering students.
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 in complete block designs.
STATS 580 / SOC 717 / SURVMETH 617 / BIOSTAT 617. 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). May not be repeated for credit.
Credits: (3).
Course Homepage: http://coursetools.ummu.umich.edu/2003/fall/survmeth/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 multistage 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, multiphase or double
sampling, ratio estimation, selection with unequal probabilities without replacement, nonresponse
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.
STATS 610(510). Theoretical Statistics I.
Section 001.
Prerequisites: MATH 450 or 451 and a course in probability or statistics. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~keener/610/index.html
Review of probability theory including: probability, conditioning, independence, random variables, standard distributions, exponential families, inequalities and a central limit theorem. Introduction to decision theory including: models, parameter spaces, decision rules, risk functions, Bayes versus classical approaches, admissibility, minim ax rules, likelihood functions and sufficiency. Estimation theory including unbiased ness, complete sufficient statistics, LehmannScheffe and RaoBlackwell theorems.
STATS 612(610). Theoretical Statistics III.
Section 001.
Prerequisites: MATH 601 and 625, and graduate standing. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
Review of probability theory
including: probability, conditioning, independence, random variables, standard distributions, exponential families, inequalities, and a central
limit theorem. Introduction to decision theory including: models, parameter spaces, decision rules, risk functions, Bayes versus classical
approaches, admissibility, minimax rules, likelihood functions, and
sufficiency. Estimation theory including unbiasedness, complete sufficient
statistics, LehmannScheffe and RaoBlackwell theorems, and various types
of estimators.
STATS 620. Theory of Probability I.
Section 001.
Instructor(s):
Anna Amirdjanova
Prerequisites: MATH 451, and graduate standing. (3). May not be repeated for credit.
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.
STATS 625 / MATH 625. Probability and Random Processes I.
Section 001.
Prerequisites: MATH 597, and graduate standing. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://www.math.lsa.umich.edu/~conlon/math625.html
See Mathematics 625.001.
STATS 710. Special Topics.
Section 001 — MULTISTAGE DECISION PROBLEMS IN ADAPTIVE TREATMENT STRATEGIES.
Prerequisites: Graduate standing and permission of instructor. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: http://www.stat.lsa.umich.edu/~samurphy/classes.html
What do Adaptive Treatment Strategies, Partially Observed Markov Decision Processes, Markov Decision Processes, Reinforcement Learning, Model Predictive Control and Medical Decision Making have in common? These are all multistage decision problems! Adaptive treatment
strategies are individually tailored treatments for chronic, relapsing disorders. In adaptive treatment strategies, we tailored the treatment level and
type through time to the individual. That is we use decision rules that assign treatment based on evolving individual information; our goal is to
make these decisions so as to maximize individual response. In this course we survey the variety of methods listed above, many of which have
been developed by other fields. In particular we focus on data driven methods for estimating the best treatment decision rules. The estimation of
best treatment decision rules from data is an area of causal inference.
GOAL: Explore how we can use social science and biological/medical data to estimate optimal decision rules.
STATS 725 / MATH 725. Topics in Advanced Probability I.
Section 001 — Topic?
Instructor(s):
Anna Amirdjanova
Prerequisites: STATS 626. Graduate standing. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
See Mathematics 725.001.
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 808. Seminar in Applied Statistics I.
Section 001 — Topic?
Instructor(s):
Anna Amirdjanova
Prerequisites: Graduate standing. (1). May not be repeated for credit.
Credits: (1).
Course Homepage: No homepage submitted.
No Description Provided. Contact the Department.
STATS 810. Literature Proseminar I.
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 mathematics 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 816. Interdisciplinary Seminar in the Physical Sciences.
Section 001 — Topic? credits?
Prerequisites: STATS 426; Graduate standing. (14). May be repeated for credit for a maximum of 12 credits.
Credits: (14).
Course Homepage: No homepage submitted.
No Description Provided. Contact the Department.
STATS 817 / PSYCH 817 / SOC 810 / EDUC 817. Interdisciplinary Seminar in Quantitative Social Science Methodology.
Section 001.
Prerequisites: Graduate standing, and graduatelevel course in STATS at the level of STAT 500 and 501. (1). May be repeated for credit for a maximum of 6 credits. This course has a grading basis of "S" or "U."
Credits: (1).
Course Homepage: http://www.qmp.isr.umich.edu/disc_series_info.cfm
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.
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 6:31 PM on Tue, Sep 23, 2003.
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