Note: You must establish a session for Winter Academic Term 2004 on wolverineaccess.umich.edu in order to use the link "Check Times, Location, and Availability". Once your session is established, the links will function. Courses in StatisticsThis page was created at 7:08 PM on Wed, Jan 21, 2004. Winter Academic Term, 2004 (January 6 - April 30)STATS 100. Introduction to Statistical Reasoning.THERE WILL BE TWO (2) EVENING EXAMS: THURS, FEB 20, 6:00-7:30 P.M. AND WEDS, APRIL 16, 6 - 8 P.M.Instructor(s): Kirsten Namesnik (namesnik@umich.edu) , Yolande Tra (ytra@umich.edu)Prerequisites & Distribution: (4). (MSA). (BS). (QR/1). May not be repeated for credit. No credit granted to those who have completed or are enrolled in SOC 210, STATS 265, 350, 400, 405, or 412, IOE 265, or ECON 404 or 405.
Credits: (4). Course Homepage: http://www.stat.lsa.umich.edu/~silvestr/stat100/ This course will provide an overview of the field of Statistics. It will expose students to statistical reasoning and concepts for dealing with uncertainty, visualizing and analyzing data, and drawing conclusions from experimental or observational data. Emphasis is on presenting underlying concepts rather than covering a variety of different methodologies. Course evaluation is based on a combination of in-class quizzes, weekly homework, a Thursday evening midterm examination, a final examination, and GSI input. The course format includes lectures and a discussion section (1 hour per week). Homework: There will be weekly homework assignments, available each Monday on the web, which will be collected and graded. Problems assigned (and their corresponding solutions) will be posted on the web for viewing and printing. There will be four in-class quizzes during the academic term. Textbook: Student Solutions Manual (optional) TI-83 Calculator — *Required.*
STATS 265 / IOE 265. Probability and Statistics for Engineers.Section 100.Instructor(s): SenPrerequisites & Distribution: MATH 116 and ENGR 101. (4). (Excl). (BS). May not be repeated for credit. No credit granted to those who have completed or are enrolled in STATS 311, 400, 405, or 412, or ECON 405. 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://coursetools.ummu.umich.edu/2004/winter/ioe/265/100.nsf Graphical Representation of Data; Axioms of Probability; Conditioning, Bayes Theorem; Discrete 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.
STATS 265 / IOE 265. Probability and Statistics for Engineers.Section 200.Instructor(s): Garcia-GuzmanPrerequisites & Distribution: MATH 116 and ENGR 101. (4). (Excl). (BS). May not be repeated for credit. No credit granted to those who have completed or are enrolled in STATS 311, 400, 405, or 412, or ECON 405. 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://coursetools.ummu.umich.edu/2004/winter/ioe/265/200.nsf 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 modeling 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! Examples include the characteristics of products leaving manufacturing lines (e.g., lifetime of a bulb, concentration of a therapeutic drug), results of laboratory experiments (e.g., growth rates of microorganisms) or processes observed over space or time (e.g., spatial distribution of soil contaminants or time series of rainfall amounts). The field of statistics deals with the collection, presentation, analysis, and use of data to make decisions, solve problems and design products and processes. The first part of the course will be devoted to the presentation of probabilistic concepts which are the building blocks of all statistical procedures that will be introduced in the second (more applied) part of the class. Homeworks and labs will allow students to apply these concepts and learn how to use basic statistical Excel functions to solve problems. The course prerequisites are MATH 116 and ENGIN 101. IOE 265 is a prerequisite for many undergraduate IOE courses (e.g., IOE 316, 366, 421, 425, 432, 441, 447, 449, 452, 460, 463, 465, 466, 474, 424/491). Text: Applied Statistics and Probability for Engineers by Douglas C. Montgomery and George C. Runger, Second Edition, 1998. Chapters 1 to 9 will be covered in this course. Grading scheme:
Course outline:
STATS 350. Introduction to Statistics and Data Analysis.THERE WILL BE TWO (2) EVENING EXAMS: THURS, FEB 12 AND APRIL 1, 6:00-7:30 PM.Instructor(s): Brenda Gunderson (bkg@umich.edu) , Yolande Tra (ytra@umich.edu) , Kirsten Namesnik (namesnik@umich.edu)Prerequisites & Distribution: (4). (NS). (BS). (QR/1). May not be repeated for credit. No credit granted to those who have completed or are enrolled in ECON 404 or 405, or IOE 265 or STATS 265, 400, 405, or 412.
Credits: (4). Course Homepage: http://www.stat.lsa.umich.edu/~bkg/stat350/ In this course, students are introduced to the concepts and applications of statistical methods and data analysis. STATS 350 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 (1.5 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. Textbook:
Mind on Statistics
STATS 400. Applied Statistical Methods.Section 001.Instructor(s): Bendek B Hansen (bbh@umich.edu)Prerequisites & Distribution: High School Algebra. (4). (Excl). (BS). (QR/1). 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/2004/winter/stats/400/001.nsf 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).
STATS 401(403). Applied Statistical Methods II.Section 001.Instructor(s): Kerby A Shedden (kshedden@umich.edu)Prerequisites & Distribution: MATH 115, and STATS 350 or 400. (4). (Excl). (BS). May not be repeated for credit. No credit granted to those who have completed or are enrolled in STATS 413. 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, 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.
STATS 404. Effective Communication in Statistics.Section 001.Instructor(s): Mary A RitterPrerequisites & Distribution: STAT 470 or 480. Permission of department required. (2). (Excl). May not be repeated for credit.
Credits: (2). Course Homepage: No homepage submitted. No Description Provided. Contact the Department.
STATS 405 / ECON 405. Introduction to Statistics.Section 001.Instructor(s): Elizaveta Levina (elevina@umich.edu)Prerequisites & Distribution: MATH 116. Juniors and seniors may elect this course concurrently with ECON 101 or 102. (4). (Excl). (BS). (QR/1). 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: http://www.stat.lsa.umich.edu/~elevina/stat405/index.html 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.Instructor(s): Edward Rothman (erothman@umich.edu)Prerequisites & Distribution: High school algebra. (4). (Excl). (BS). May not be repeated for credit. No credit granted to those who have completed or are enrolled in STATS 170. Credits: (4). Course Homepage: http://coursetools.ummu.umich.edu/2004/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:
STATS 412. Introduction to Probability and Statistics.Section 001.Instructor(s): Elizaveta Levina (elevina@umich.edu)Prerequisites & Distribution: Prior or concurrent enrollment in MATH 215 and EECS 183. (3). (Excl). (BS). 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: 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 413. The General Linear Model and Its Applications.Section 001.Instructor(s): Yongyun Shin (choil@umich.edu)Prerequisites & Distribution: STATS 350 and MATH 217; concurrent enrollment in STATS 425. Students who have not taken MATH 217 should elect STATS 401. (4). (Excl). (BS). May not be repeated for credit. Two credits granted to those who have completed STATS 403. 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.
STATS 425 / MATH 425. Introduction to Probability.Instructor(s): Statistics facultyPrerequisites & Distribution: MATH 215, 255, or 285. (3). (Excl). (BS). 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 facultyPrerequisites & Distribution: MATH 215, 255, or 285. (3). (Excl). (BS). May not be repeated for credit. Credits: (3). Course Homepage: No homepage submitted.
STATS 425 / MATH 425. Introduction to Probability.Section 003, 007.Instructor(s): Sergey Fomin (fomin@umich.edu)Prerequisites & Distribution: MATH 215, 255, or 285. (3). (Excl). (BS). May not be repeated for credit. Credits: (3). Course Homepage: http://www.math.lsa.umich.edu/~fomin/425w04.html
STATS 426. Introduction to Theoretical Statistics.Section 001.Instructor(s): Moulinath Banerjee (moulib@umich.edu)Prerequisites & Distribution: STATS 425. (3). (Excl). (BS). 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:
STATS 430. Applied Probability.Section 001.Instructor(s): George Michailidis (gmichail@umich.edu)Prerequisites & Distribution: STATS 425. (3). (Excl). (BS). May not be repeated for credit. Credits: (3). Course Homepage: No homepage submitted. Review of probability theory; introduction to random walks; counting and Poisson processes; Markov chains in discrete and continuous time; equations for stationary distributions; introduction to Brownian motion. Selected applications such as branching processes, financial modeling, genetic models, the inspection paradox, inventory and queuing problems, prediction, and/or risk analysis. Selected optional topics such as hidden Markov chains, martingales, renewal theory, and/or stationary process.
STATS 466 / IOE 466 / MFG 466. Statistical Quality Control.Section 001.Instructor(s): Justin Wayne KilePrerequisites & Distribution: STATS 265 and 401 or IOE 366. (4). (Excl). (BS). May not be repeated for credit. 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://coursetools.ummu.umich.edu/2004/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 470. Introduction to the Design of Experiments.Section 001.Instructor(s): Nancy S Boudreau (nboudre@umich.edu)Prerequisites & Distribution: STATS 401. (4). (Excl). (BS). 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 499. Honors Seminar.Instructor(s):Prerequisites & Distribution: Permission of departmental Honors advisor. (2-3). (Excl). (BS). (INDEPENDENT). May not be repeated for credit. Credits: (2-3). Course Homepage: No homepage submitted. Advanced topics, reading and/or research in applied or theoretical statistics.
STATS 501. Applied Statistics II.Section 001.Instructor(s): Julian J Faraway (faraway@umich.edu)Prerequisites & Distribution: STATS 500. (3). (Excl). (BS). 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, Log-linear models, Random and mixed effects models, Smoothing and non-parametric regression, Generalized Additive models, Regression and classification trees, Neural Networks. Course pack. The following books are recommended
Assessment: Assignments worth 30%; Midterm worth 30%; Final worth 40%. All examinations are open book.
STATS 503. Applied Multivariate Analysis.Section 001.Instructor(s): George Michailidis (gmichail@umich.edu)Prerequisites & Distribution: STATS 500. (3). (Excl). (BS). May not be repeated for credit. Credits: (3). Course Homepage: No homepage submitted. 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.
STATS 525 / MATH 525. Probability Theory.Section 001.Instructor(s): Gautam BharaliPrerequisites & Distribution: MATH 451 (strongly recommended) or 450. STATS 425 would be helpful. (3). (Excl). (BS). May not be repeated for credit. Credits: (3). Course Homepage: No homepage submitted.
STATS 526 / MATH 526. Discrete State Stochastic Processes.Section 001.Instructor(s): Charles R DoeringPrerequisites & Distribution: STATS 525 or EECS 501. (3). (Excl). (BS). May not be repeated for credit. 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; Chapman-Kolmogorov 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.
This page was created at 7:08 PM on Wed, Jan 21, 2004.
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