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Courses in Statistics

These pages are no longer maintained. Consult the new Course Guide at: http://www.lsa.umich.edu/lsa/cg_subjectlist/0,2030,8,00.html?show=20&termArray=f_04_1510&cgtype=gr

This page was created at 11:04 PM on Mon, May 10, 2004.

Fall Academic Term 2004 (September 7 - December 23)

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STATS 400. Applied Statistical Methods.

Instructor(s):

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: No homepage submitted.

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).

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STATS 401. Applied Statistical Methods II.

Instructor(s):

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, two-sample problems, one-way analysis of variance; multiple linear regression, diagnostics and model selection; two-way analysis of variance, multiple comparisons, and other selected topics. Three hours of lecture supplemented by one and one-half hours of laboratory.

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STATS 405 / ECON 405. Introduction to Statistics.

Instructor(s): Sedo

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 ECON 405.001.

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STATS 406. Introduction to Statistical Computing.

Instructor(s):

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: No homepage submitted.

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 one and one-half hour laboratory session each week.

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STATS 412. Introduction to Probability and Statistics.

Instructor(s):

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.

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.

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STATS 425 / MATH 425. Introduction to Probability.

Instructor(s): STATISTICS 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.

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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 MATH 425.

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STATS 426. Introduction to Theoretical Statistics.

Instructor(s):

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 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
```

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

Instructor(s): Jianjun Shi

Prerequisites: STATS 265 and 401 or IOE 366. (3). May not be repeated for credit. CAEN lab access fee required for non-Engineering students.

Credits: (3).

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

Course Homepage: No homepage submitted.

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; and charting, smoothing, forecasting, and prediction of discrete time series.

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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.

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STATS 500. Applied Statistics I.

Instructor(s):

Prerequisites: MATH 417, and STATS 350 or 426. (3). May not be repeated for credit.

Credits: (3).

Course Homepage: No homepage submitted.

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, Gauss-Markov 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.

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STATS 500. Applied Statistics I.

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

Prerequisites: MATH 417, and STATS 350 or 426. (3). May not be repeated for credit.

Credits: (3).

Course Homepage: No homepage submitted.

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, Gauss-Markov 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.

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STATS 504. Statistical Consulting.

Instructor(s):

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 and social sciences; students will be expected to analyze data and write reports.

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STATS 505 / ECON 671. Econometric Analysis I.

Instructor(s): Lutz Kilian (lkilian@umich.edu)

Prerequisites: Permission of instructor. (3). May not be repeated for credit.

Credits: (3).

Course Homepage: No homepage submitted.

See ECON 671.001.

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STATS 525 / MATH 525. Probability Theory.

Instructor(s):

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 MATH 525.001.

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STATS 535 / IOE 562. Reliability.

Instructor(s):

Prerequisites: STATS 425 and 426 (or IOE 316 and 366). (3). May not be repeated for credit. CAEN lab access fee required for non-Engineering students.

Credits: (3).

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

Course Homepage: No homepage submitted.

No Description Provided. Contact the Department.

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STATS 550 / IOE 560 / OMS 603. Bayesian Decision Analysis.

Instructor(s): Stephen M Pollock

Prerequisites: STATS 426 OR IOE 366. (3). May not be repeated for credit. CAEN lab access fee required for non-Engineering students. Business School Network fee may be required for non-Business students.

Credits: (3).

Lab Fee: CAEN lab access fee required for non-Engineering students. Business School Network fee may be required for non-Business students.

Course Homepage: No homepage submitted.

Topics:

• Axiomatic foundations for, and assessment of, probability and utility;
• formulation of decision problems;
• 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 decision-making.

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STATS 570 / IOE 570. Design of Experiments.

Instructor(s):

Prerequisites: STATS 500 or background in regression. Graduate standing. (3). May not be repeated for credit. CAEN lab access fee required for non-Engineering students.

Credits: (3).

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

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STATS 575 / ECON 678. Econometric Theory I.

Instructor(s):

Prerequisites: ECON 671, 672, and 600. (3). May not be repeated for credit.

Credits: (3).

Course Homepage: No homepage submitted.

See ECON 678.001.

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STATS 580 / SOC 717 / SURVMETH 617 / BIOSTAT 617. Methods and Theory of Sample Design.

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: No homepage submitted.

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.

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STATS 610(510). Theoretical Statistics I.

Instructor(s):

Prerequisites: MATH 450 or 451 and a course in probability or statistics. (3). May not be repeated for credit.

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.

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STATS 612(610). Theoretical Statistics III.

Instructor(s):

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, Lehmann-Scheffe and Rao-Blackwell theorems and various types of estimators.

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STATS 620. Theory of Probability I.

Instructor(s):

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 Moivre-Laplace 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 Borel-Cantelli lemmas and the zero-one law. The course will additionally cover: integration, convergence theorems, inequalities, Fubini's theorem, the Radon-Nikodym theorem, distribution functions, expectations, and the strong law of large numbers.

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STATS 626 / MATH 626. Probability and Random Processes II.

Instructor(s):

Prerequisites: STATS 625. Graduate standing. (3). May not be repeated for credit.

Credits: (3).

Course Homepage: No homepage submitted.

See MATH 626.001.

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STATS 700(600). Special Topics in Applied Statistics I.

Instructor(s):

Prerequisites: STATS 501 and Graduate standing. (1-4). May not be repeated for credit.

Credits: (1-4).

Course Homepage: No homepage submitted.

No Description Provided. Contact the Department.

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STATS 725 / MATH 725. Topics in Advanced Probability I.

Instructor(s):

Prerequisites: STATS 626. Graduate standing. (3). May not be repeated for credit.

Credits: (3).

Course Homepage: No homepage submitted.

See MATH 725.001.

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Instructor(s):

Prerequisites: Consent of instructor required (Prerequisites enforced at registration). Graduate standing and permission of instructor. (1-6). (INDEPENDENT). May be elected for a maximum of 5 credits. May be elected more than once in the same term.

Credits: (1-6).

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.

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STATS 808. Seminar in Applied Statistics I.

Instructor(s):

Prerequisites: Graduate standing. (1). May not be repeated for credit.

Credits: (1).

Course Homepage: No homepage submitted.

No Description Provided. Contact the Department.

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STATS 810. Literature Proseminar I.

Instructor(s):

Prerequisites: Consent of instructor required (Prerequisites enforced at registration). 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.

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STATS 816. Interdisciplinary Seminar in the Physical Sciences.

Instructor(s):

Prerequisites: STATS 426; Graduate standing. (1-4). May be repeated for credit for a maximum of 12 credits.

Credits: (1-4).

Course Homepage: No homepage submitted.

No Description Provided. Contact the Department.

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STATS 817 / PSYCH 817 / SOC 810 / EDUC 817. Interdisciplinary Seminar in Quantitative Social Science Methodology.

Instructor(s): Yu Xie (yuxie@umich.edu)

Prerequisites: Graduate standing, and graduate-level 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: 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.

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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. (1-8). (INDEPENDENT). May be repeated for credit. This course has a grading basis of "S" or "U."

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.

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STATS 993. Graduate Student Instructor Training Program.

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

Prerequisites: Graduate standing. (1). May not be repeated for credit. This course has a grading basis of "S" or "U."

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 term. Beginning graduate student instructors are required to register for this course.

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STATS 995. Dissertation/Candidate.

Instructor(s):

Prerequisites: Graduate School authorization for admission as a doctoral Candidate (Prerequisites enforced at registration). (8). (INDEPENDENT). May be repeated for credit. This course has a grading basis of "S" or "U."

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.

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These pages are no longer maintained. Consult the new Course Guide at: http://www.lsa.umich.edu/lsa/cg_subjectlist/0,2030,8,00.html?show=20&termArray=f_04_1510&cgtype=gr

This page was created at 11:04 PM on Mon, May 10, 2004.