Take me to the Winter Term '00 * Time Schedule * for Statistics.

**Prerequisites & Distribution:** No credit granted to those who have completed or are enrolled in Soc. 210, Stat. 265, 311, 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/100woo/**

This course is designed to provide an overview of the field of statistics. Course topics include methods of analyzing and summarizing data, statistical reasoning as a means of learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Basic fallacies in common statistical analyses and reasoning are discussed and proper methods indicated. Alternative approaches to statistical inference are also discussed. The course emphasis is on presenting basic underlying concepts rather than on covering a wide variety of different methodologies. Course evaluation is based on a combination of a Thursday evening midterm examination, a final examination, and teaching fellow input. The course format includes three lectures and a laboratory (1 hour per week).

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**Prerequisites & Distribution:** No credit granted to those who have completed or are enrolled in Soc. 210, Stat. 265, 311, 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/100W00/**

This course is designed to provide an overview of the field of statistics. Course topics include methods of analyzing and summarizing data, statistical reasoning as a means of learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Basic fallacies in common statistical analyses and reasoning are discussed and proper methods indicated. The course emphasis is on presenting basic underlying concepts rather than on covering a wide variety of different methodologies. Course evaluation is based on a combination of a Thursday evening midterm examination, a final examination, in-class quizzes, weekly homework, and lab participation. The course format includes three lectures (3 hours per week) and a laboratory (1 hour per week).

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**Prerequisites & Distribution:** No credit granted to those who have completed or are enrolled in Soc. 210, Stat. 265, 311, 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/100W00/**

This course is designed to provide an overview of the field of statistics. Course topics include methods of analyzing and summarizing data, statistical reasoning as a means of learning from observations (experimental or sample), and techniques for dealing with uncertainties in drawing conclusions from collected data. Basic fallacies in common statistical analyses and reasoning are discussed and proper methods indicated. The course emphasis is on presenting basic underlying concepts rather than on covering a wide variety of different methodologies. Course evaluation is based on a combination of a Thursday evening midterm examination, a final examination, in-class quizzes, weekly homework, and lab participation. The course format includes three lectures (3 hours per week) and a laboratory (1 hour per week).

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Waitlist Code: 3 |

**Prerequisites & Distribution:** Only first-year students, including those with sophomore standing, may pre-register for First-Year Seminars. All others need permission of instructor. (3). (MSA). (QR/1).

**Credits:** (3).

**Course Homepage: No Homepage Submitted.**

This course will emphasize problem solving and modeling. To achieve this end, students will work together in class attempting to solve various problems posed by the instructor. Hopefully with a bit of gentle guidance, the students will be able to create models and deduce the basic concepts necessary for solution. Students will be asked to write up solutions and work on a project. Grades will be determined from this work and class participation. Problems from the course will be drawn primarily from Markov chains with a finite state space, dynamic programming, again with a finite state space, and game theory. Possible examples include: gambler's ruin; expected run lengths in coin tossing until a specified string is obtained and chances that one string will occur before another; optimal strategies in sports and gambling; optimal replacement strategies; minimax solutions for finite state two-person zero sum games.

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Waitlist Code: 3 |

**Prerequisites & Distribution:** No credit granted to those who have completed or are enrolled in Statistics 408. (4). (MSA). (BS). (QR/1).

**Credits:** (4).

**Course Homepage: No Homepage Submitted.**

The objective of this course is to introduce students to the learning process in a non-deterministic environment. An appreciation for measurement, bias, and variation is essential to formulate questions and learn about things. Underlying this course is the Edwards Deming philosophy. Deming, an American statistician, was invited to Japan in the early 1950's to help improve the quality of mass produced items. His success in Japan is, in part, responsible for our current balance of trade deficit; and here the Ford Motor Co. has also attained a larger market share as a result of his ideas. Implementation of the Deming message requires a critical appreciation of variation and the scientific method. Specifically, we will discuss:

- Historical attempts to learn and the advent of the modern scientific method.
- The differences between special or assignable causes and common causes of variation. Before we can learn how a process operates, the process must be stable.
- Differences between observational and controlled randomized studies and associated ethical issues.
- The 'what' and 'how' of measurement and the quantification of uncertainty-subjective and frequency notions of probability.
- Understanding bias and variation.
- How to use bias to design efficient studies.
- Differences between enumerative and analytic studies.

Many of the ideas will be introduced through experimentation *(e.g., * the red bead and funnel experiments), and the mathematical level will not require more than a modest background in high school algebra. The course format includes three lectures and a laboratory (1.5 hours per week).

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Waitlist Code: 3 |

**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.

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

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**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/402W00/**

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 GIVEN WEDNESDAY EVENINGS, a final examination, weekly homework, and lab participation, statistical analysis. Include: Term exams on Wednesday, October 13, 6-8 P.M. and Wednesday November 17, 6-8 P.M.

Check Times, Location, and Availability | Cost: 2 |
Waitlist Code: 3 |

**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/402W00/**

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 GIVEN WEDNESDAY EVENINGS, a final examination, weekly homework, and lab participation, statistical analysis. Include: Term exams on Wednesday, February 16, 6-8 P.M. and Wednesday March 29, 6-8 P.M.

Check Times, Location, and Availability | Cost: 2 |
Waitlist Code: 3 |

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

**Credits:** (4).

**Course Homepage: http://www.stat.lsa.umich.edu/~kshedden/Courses/Stat403/index.html**

Intermediate topics in multiple linear regression and the analysis of covariance, stressing applications: least squares estimates, test of hypotheses, prediction analysis, residual analysis, multicollinearity, and the variable selection techniques; fixed and random effects models in ANOVA; multiple comparisons, randomized blocks, Latin squares, nested and hierarchical designs; and robust procedures, as time permits. Three hours of lecture supplemented by one and one-half hours of laboratory.

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**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: http://www-personal.umich.edu/~epena/stat405.html**

Principles of statistical inference, including: probability, experimental and theoretic derivation of sampling distributions, hypothesis testing, estimation, and simple regression.

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Waitlist Code: 3 |

**Prerequisites & Distribution:** High School Algebra. No credit granted to those who have completed or are enrolled in Statistics 170. (4). (Excl). (BS).

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**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. 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.

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**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. 402. (3). (MSA). (BS).

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**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.

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

**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. Weekly problem sets, two hourly exams, and one final exam.

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**Prerequisites & Distribution:** Statistics 425. (3). (Excl).

**Credits:** (3).

**Course Homepage: http://www.stat.lsa.umich.edu/~gmichail/stat430-W00/**

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.

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**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.

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

**Credits:** (4).

**Course Homepage: No Homepage Submitted.**

Motivating examples, abstraction to: populations, variables, parameters, * etc.; * samples and sample designs, probability versus convenience samples, target versus sampled populations, frames, inclusion probabilities, joint inclusion probabilities, statistics, sampling distributions, and Horvitz-Thompson estimators; simple random samples (with and without replacement), binomial and hypergeometric distributions, sample size determinations; cluster sample designs; systematic sample designs; stratified sample designs, including sample size determination, Neyman allocation, proportional allocation, * etc.; * two and multiple stage designs, estimation and optimal design; combination designs, * e.g., * stratified cluster samples, * etc.; * non-sampling errors and biases, non-response (unit and item), response bias and error, and possible preventatives and cures; and special topics as time allows: * e.g., * capture-recapture sampling, Bayesian views, area sampling, * etc. * There will be weekly assignments and a final exam. Class format is three hours of lecture and one hour of laboratory per week.

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**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.

Check Times, Location, and Availability | Cost: 2 |
Waitlist Code: 3 |

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

**Credits:** (3).

**Course Homepage: http://www.stat.lsa.umich.edu/~faraway/stat501/**

Introduction to Generalized Linear Models; Estimation and inference; Residuals and diagnostics; Analysis of binary and polytomous data; Log-linear models; Models for survival data; Smoothing and non-parametric regression; Generalized Additive models; Regression and classification trees; Neural Networks.

Check Times, Location, and Availability | Cost: 3 |
Waitlist Code: 3 |

**Prerequisites & Distribution:** Stat. 403 or 500. (3). (Excl). (BS). May be repeated for a total of eight credits.

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**Prerequisites & Distribution:** Stat. 510. (3). (Excl). (BS).

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**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.**

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**Prerequisites & Distribution:** Math. 525/Stat. 525, or EECS 501. (3). (Excl). (BS).

**Credits:** (3).

**Course Homepage: No Homepage Submitted.**

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**Prerequisites & Distribution:** Stat. 426. (3). (Excl). (BS).

**Credits:** (3).

**Course Homepage: No Homepage Submitted.**

Introduction to modern time series models and methods including identification and estimation of univariate and multivariate autoregressive moving average models for discrete time covariance stationary processes, spectrum estimation and inference, and state space methods.

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**Prerequisites & Distribution:** Stat. 426. (3). (Excl). (BS).

**Credits:** (3).

**Course Homepage: No Homepage Submitted.**

First half covers theory and applications of rank and randomization tests: sampling and randomization models, randomization t-test, Wilcoxon rank sum and signed rank tests, Kruskal-Wallis test, asymptotic results under randomization, relative efficiency; second half covers theory and applications of nonparametric regression: smoothing methods, including kernel estimators, local linear regression, smoothing splines, and regression splines, methods for choosing the smoothing parameter, including unbiased risk estimation and cross-validation, introduction to additive models.

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**Prerequisites & Distribution:** Stat. 575. (3). (Excl). (BS).

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