Take me to the Winter
Term '99 * 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: No Homepage Submitted.**

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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Code: 3 |

**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: No Homepage Submitted.**

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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2 |
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Code: 3 |

**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/100W99/class.html**

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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2 |
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Code: 3 |

**Prerequisites & Distribution:** (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: (1) Historical attempts to learn and the
advent of the modern scientific method. (2) 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. (3) Differences between
observational and controlled randomized studies and associated ethical issues.
(4) The 'what' and 'how' of measurement and the quantification of uncertainty-subjective
and frequency notions of probability. (5) Understanding bias and variation.
(6) How to use bias to design efficient studies. (7) 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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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).

**Credits:** (4).

**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.

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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/402W99/class.html**

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 three lectures 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 micro-computer package and the Macintosh computer. Course evaluation is based on a combination of two examinations GIVEN WEDNESDAY EVENINGS, a final examination, and teaching fellow input.

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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/402W99/class.html**

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 three lectures 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 micro-computer package and the Macintosh computer. Course evaluation is based on a combination of two examinations GIVEN WEDNESDAY EVENINGS, a final examination, and teaching fellow input.

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/402W99/class.html**

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 three lectures 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 micro-computer package and the Macintosh computer. Course evaluation is based on a combination of two examinations GIVEN WEDNESDAY EVENINGS, a final examination, and teaching fellow input.

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/402W99/class.html**

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2 |
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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/402W99/class.html**

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2 |
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Code: 3 |

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

**Credits:** (4).

**Course Homepage: http://www-personal.umich.edu/~kutsyy/classes/winter99/403.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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Code: 3 |

**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.**

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

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Code: 3 |

**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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Code: 3 |

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

**Credits:** (3).

**Course Homepage: No Homepage Submitted.**

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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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Code: 3 |

**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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Code: 3 |

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

**Credits:** (3).

**Course Homepage: http://www.stat.lsa.umich.edu/~robb/STATISTICS426/**

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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Code: 3 |

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

**Credits:** (3).

**Course Homepage: No Homepage Submitted.**

This is a second course in probability with two related goals, to illustrate the wide applicability of probability theory and to introduce models in which there is some dependence among the variables. The emphasis will be on the models, results, and their applications, as opposed to proofs and derivations. The main topics are as follows:

- Review of Probability and Discrete Random Variables.
- Simple Random Walks and the Gamblerís Ruin Problem.
- Markov Chains in Discrete Time.
- Review of Continuous Distributions; Hazard Functions.
- Exponential Distributions and Poisson Processes.
- Markov Chains in Continuous Time.
- More on Random Walks; Laws of Large Numbers and the Central Limit Theorem.
- Simulation: Using the Law of Large Numbers to Compute Probabilities.
- Renewal Theory and its Ramifications; the Inspection Paradox.
- Q ueueing Models.
- Martingales and Gambling Systems.
- Introduction to Brownian Motion.
- Introduction to Financial Models.

The prerequisite is Math/Stat 425 or equivalent. The text for the course is Introduction to Probability Models, by Sheldon Ross. Questions may be addressed to Michael Woodroofe at 1433 Mason Hall (763-3495) or by e-mail (michaelw@umich.edu).

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**Prerequisites & Distribution:** Stat. 265 or 311.
(3). (Excl). (BS).

**Credits:** (3).

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

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2 |
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Code: 3 |

**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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Code: 3 |

**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.

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Code: 3 |

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

**Credits:** (3).

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

A variety of topics in applied statistics will be covered in the course. The main topics are survey sampling methods including: simple random sampling, stratification, cluster sampling, systematic sampling and multistage sampling methods. Survival analysis including: hazard and survival functions, censoring, Kaplan-Meier estimation, graphical methods and proportional hazards models. Bootstrap and jackknife methods and their uses. Topics in time series analysis including: autocorrelation functions, stationarity, identification, estimation and forecasting with ARIMA models and spectra. Non-parametric density estimation including: kernels, cross validation, splines, and the penalized maximum likelihood estimators. Discriminant analysis including: linear and quadratic discriminators, relation to regression and non-parametric approaches.

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Code: 3 |

**Prerequisites & Distribution:** Stat. 403 or 500.
(1-4). (Excl). (BS). May be repeated for a total of eight credits.

**Credits:** (1-4).

**Course Homepage: No Homepage Submitted.**

Applications of statistics to problems in the sciences and social sciences; students will be expected to analyze data and write reports.

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Code: 3 |

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

**Credits:** (3).

**Course Homepage: No Homepage Submitted.**

Topics covered will include: hypothesis testing and related topics such as size, power, similarity and optimality properties. Likelihood ratio tests, generalized likelihood ratio tests, decision theory, and Bayes approaches. Sequential procedures, large sample theory, and various other topics.

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Code: 3 |

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

**Credits:** (3).

**Course Homepage: No Homepage Submitted.**

Decomposition of series; trend
and regression as a special case of time series; cyclic components; smoothing
techniques; the variate difference method; representations including spectogram, periodogram, * etc., * stochastic difference equations, autoregressive
schemes, moving averages; large sample inference and predictions; covariance
structure and spectral densities; hypothesis testing and estimation; applications
and other topics.

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

**Credits:** (3).

**Course Homepage: No Homepage Submitted.**

Axiomatic foundations for personal probability and utility; interpretation and assessment of personal probability and utility; formulation of Bayesian decision problems; risk functions, admissibility likelihood principle and properties of likelihood functions; natural conjugate prior distributions; improper and finitely additive prior distributions; examples of posterior distributions, including the general regression model and contingency tables; Bayesian credible intervals and hypothesis tests; application to a variety of decision-making situations.

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Code: 3 |

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

**Credits:** (3).

**Course Homepage: http://www.econ.lsa.umich.edu/~ssakata/econ776/index.html**

This course is the second course of the econometric theory sequence. It rigorously examines (a) the properties of the quasi-maximum likelihood and generalized method-of-moment estimators and (b) statistical inference based on them. Economics 673, 674, and Statistics 575, or their equivalents are prerequisites for this course.

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Code: 4 |

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