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Courses in Statistics (Division 489)

This page was created at 4:08 PM on Wed, Dec 13, 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 has been added to or changed in Statistics this week go to What's New This Week.

There will be One (1) Midterm Exam on DATE TBA, 6-8 p.m. for Statistics 100.

Prerequisites & Distribution: No credit granted to those who have completed or are enrolled in Soc. 210, Stat. 250, 402, 405, or 412, or Econ. 404 or 405. (4). (MSA). (BS). (QR/1).

Credits: (4).

Course Homepage: http://www.stat.lsa.umich.edu/~bkg/100F00/

Provides an overview of the field of statistics, including methods of summarizing and analyzing data, statistical reasoning for learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Emphasis is on presenting underlying concepts rather than covering a variety of different methodologies. Course evaluation is based on a combination of a Thursday evening midterm examination, a final examination, and GSI input. The course format includes lectures and a discussion section (1 hour per week).

Section 001.

Instructor(s): Stephen Pollock (pollock@umich.edu)

Prerequisites & Distribution: Math. 116 and Engin. 101. No credit granted to those who have completed or are enrolled in Stat. 311, 405, or 412, or Econ. 405. (4). (Excl). (BS). CAEN lab access fee required for non-Engineering students.

Credits: (4).

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

Course Homepage: http://www.engin.umich.edu/class/ioe265/

Graphical representation of Data; axioms of Probability; conditioning, Bayes Theorem; discreet distributions (Geometric, Binomial, Poisson); continuous distributions (normal exponential, Weibull), point and interval estimation, likelihood functions, test of hypotheses for Means, Variances, and Proportions for one and two populations.

Course Goals

• Main Goal: To provide students a working knowledge of probability modeling and engineering statistics, and their application to industrial situations.
• Sub-Goals:
  1. To reinforce problem solving skills
  2. To further students' abilities in analytical thinking.

Text: Applied Statistics and Probability for Engineers, Douglas C. Montgomery and George C. Runger, Second Edition, 1998.

Engineering is divided into two worlds: deterministic and probabilistic. Your math, physics, and chemistry course preparation to date has concentrated on "deterministic" models: a given set of inputs or conditions repeatedly produce a fixed, completely predictable output. IOE 265 launches your modelling skills into a totally new dimension... the much more realistic situation wherein a given set of inputs or conditions produce random (or "chance" or "probabilistic" or "stochastic" ) outcomes!

To learn this way of thinking requires time and persistence. Please do not underestimate the time and energy commitments required to master dealing with uncertainty. Budget a minimum of 12 hours of study outside of class each week. This course is designed for engineering students with skills and knowledge in mathematical techniques, including algebra and calculus. Do not confuse this course with similar-sounding ones in LS&A that are not calculus-based!

Study Tips: If the textbook has a "blue box" drawn around it... understand it! If Pollock says "This is important...", write it down and understand it!

There will be Two (2) Evening Exams for Statistics 402, to be given on DATES TBA, from 6-8 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). (NS). (BS). (QR/1).

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 analysis-computer package. Course evaluation is based on a combination of two examinations, a final examination, weekly homework, and lab participation.

Section 001.

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

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). (MSA). (BS). (QR/1).

Credits: (4).

Course Homepage: No Homepage Submitted.

See Economics 405.001.

Instructor(s): Kerby Shedden (kshedden@umich.edu)

Prerequisites & Distribution: One of Stat. 402, 405, 412, or 425. (4). (Excl). (BS).

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 hands-on experience in carrying out data analysis on a computer and in implementing simple algorithms in a low-level language.

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.

Section 001.

Instructor(s): P Jeganathan (jegan@umich.edu)

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). (MSA). (BS).

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.

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). (Excl). (BS).

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.

Instructor(s):

Prerequisites & Distribution: Math. 215, 255, or 285. (3). (MSA). (BS).

Credits: (3).

Course Homepage: No Homepage Submitted.

See Mathematics 425..

Instructor(s):

Prerequisites & Distribution: Math. 215, 255, or 285. (3). (MSA). (BS).

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.

Section 001.

Instructor(s): Julie Horrocks (jhorrock@umich.edu), jhorrock

Prerequisites & Distribution: Stat. 425. (3). (MSA). (BS).

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.

Section 001.

Instructor(s): Jianming Shi

Prerequisites & Distribution: Stat. 265 and Stat 403 or IOE 366. (4). (Excl). (BS). CAEN lab access fee required for non-Engineering students.

Credits: (4).

Lab Fee: CAEN lab access fee required for non-Engineering 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 662-3201)

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%

Instructor(s): Jeff Wu

Prerequisites & Distribution: Stat. 402. (4). (Excl). (BS).

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.

Topic? (? credits).

Prerequisites & Distribution: Permission of departmental Honors advisor. (2-3). (Excl). (INDEPENDENT).

Credits: (2-3).

Course Homepage: No Homepage Submitted.

Advanced topics, reading and/or research in applied or theoretical statistics.

Section 001.

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

Prerequisites & Distribution: Math. 417, and Stat. 402 or 426. (3). (Excl). (BS).

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, 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. 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 S-PLUS 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 S-Plus and R on the web.

Assessment: There will be an in-class, 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.

Section 001.

Instructor(s): George Michailidis

Prerequisites & Distribution: Stat. 500. (3). (Excl). (BS).

Credits: (3).

Course Homepage: http://www.stat.lsa.umich.edu/~gmichail/stat503-F00/

Topics in applied multivariate analysis including Hotelling's T² 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.

Section 001.

Instructor(s): Thomas Ryan

Prerequisites & Distribution: Stat. 403 or 500. (3). (Excl). (BS). 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.

Section 001.

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

Prerequisites & Distribution: Permission of instructor. (3). (Excl). (BS).

Credits: (3).

Course Homepage: No Homepage Submitted.

This course is designed for first-year 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.

Section 001.

Instructor(s): Robert Keener

Prerequisites & Distribution: Math. 450 or 451, and a course in probability or statistics. (3). (Excl). (BS).

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.

Instructor(s):

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). (Excl). (BS).

Credits: (3).

Course Homepage: No Homepage Submitted.

See Mathematics 525.001.

Section 001.

Instructor(s): Peter Lenk (plenk@umich.edu)

Prerequisites & Distribution: Stat. 425. (3). (Excl). (BS). CAEN lab access fee required for non-Engineering students.

Credits: (3).

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

Section 001.

Instructor(s): Shinichi Sakata (ssakata@umich.edu)

Prerequisites & Distribution: Math. 417 and 425 or Econ. 653, 654, 673, and 674. (3). (Excl). (BS).

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).

This page was created at 4:08 PM on Wed, Dec 13, 2000.

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