**100(300). Introduction to Statistical Reasoning. *** No
credit granted to those who have completed or are enrolled in
Soc. 210, Poli.Sci. 280, Stat. 402, 311, 405, or 412, or Econ.
404. (4). (NS). *

This course is designed to provide an overview of the field 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 two Thursday evening midterm examinations, a final examination
and a teaching fellow input. The course format includes three
lectures and a laboratory (1 hour per week). Cost:2 WL:3

**170(270). The Art of Scientific Investigation. *** (4).
(NS). *

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 philosphy. 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 3 lectures and a laboratory.
(1.5 hours per week). Cost:2 WL:3 (Rothman)

**311/I.O.E. 365. Engineering Statistics. *** Math.
215 or equivalent. No credit granted to those who have completed
or are enrolled in Stat. 405 or 412. One credit granted to those
who have completed Stat. 402. (4). (Excl). *

Analysis of engineering data associated with stochastic industrial
processes. Topics include: fundamentals of distribution analyses;
process model identification, estimation, testing of hypothesis, validation procedures, and evaluation of models by regression
and correlation. Students are required to use the MTS computer
system for problem solving. (Brimacombe)

**402. Introduction to Statistics and Data Analysis.
*** No credit granted to those who have completed or
are enrolled in Econ. 404 or Stat. 311, 405, or 412. (4). (NS). *

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 three examinations GIVEN WEDNESDAY EVENINGS, a final examination and teaching fellow input. Cost: 2 WL: 3

**403. Introduction to Statistics and Data Analysis II.
*** Stat. 402. (4). (Excl). *

APPLIED REGRESSION. The course will also cover various topics
associated with the general linear model emphasizing applications.
Topics include: multiple regression, variable selection, stepwise
regression, residual analysis, analysis of variance models, covariance
analysis and principal components. OTHER TOPICS. As time allows, the course may cover some aspects of probit and logit analyses, analysis of time series data, reliability analysis, and topics
in experimental design. Three hours of lecture and one and one-half
hours of lab per week. Cost:2 WL:3 (Smith)

**405/Econ. 405. Introduction
to Statistics. *** Math. 115 or permission of instructor.
Juniors and seniors may elect concurrently with Econ. 201 and 202. No credit granted to those who have completed or are enrolled
in Stat. 311 or 412. Students with credit for Econ. 404 can only
elect Stat. 405 for 2 credits and must have permission of instructor.
(4). (Excl). *

Principles of statistical inference, including: probability, experimental
and theoretic derivation of sampling distributions, hypothesis
testing, estimation, and simple regression. Cost:2 WL:3 (Hill)

**412. Introduction to Probability and Statistics. *** Prior
or concurrent enrollment in Math. 215 and CS 183. No credit granted
to those who have completed or are enrolled in 311 or 405. One
credit granted to those who have completed Stat. 402. (3). (Excl). *

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, including assignments which require the use
of MTS, two midterms, and a final exam. Cost:3 WL:3 (Jeganathan)

**425/Math. 425. Introduction to Probability. *** Math.
215. (3). (N.Excl). *

See Mathematics 425.

**426. Introduction to Mathematical Statistics. *** Stat.
425. (3). (NS). *

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. WL:3 (Muirhead)

**470. The Design of Scientific Experiments. *** Stat.
311, 402, 412, or 426; or permission of instructor. (4). (Excl). *

The objective of this course is to introduce students to the process
of planning, designing and implementation of a study. Includes
enumerative, Monte Carlo, observational and controlled randomized
experimentation. Emphasis is on the conceptual framework not on the mathematical theory of design (e.g., Statistics 570). Cost:
3 WL: 3 (Ericson)

**501. Applied Statistics II. *** Stat. 500
or permission of instructor. (3). (Excl). *

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 Bayesain methods, robust estimation and survey sampling. Applications and data analysis
using the computer will be stressed. Cost:3 WL:3 (J. Sun)

**504. Seminar on Statistical Consulting. *** Stat.
403 or 500. (1-4). (Excl). May be repeated for a total of 8 credits. *

Applications of statistics to problems in the sciences; students
will be expected to analyze data and write reports. Cost:2 WL:3
(Ericson)

**511. Mathematical Statistics II. *** Stat.
510. (3). (Excl). *

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. Cost:3 WL:3 (Keener)

**525(510)/Math. 525. Probability Theory. *** Math.
450 or 451; or permission of instructor. Students with credit
for Math. 425/Stat. 425 can elect Math. 525/Stat. 525 for only
1 credit. (3). (Excl). *

See Math 525.

**526/Math. 526. Discrete
State Stochastic Processes. *** Math. 525, or Stat. 525, or EECS 501. (3). (Excl). *

Generating functions; recurrent events and the renewal theorem;
random walks, Markov chains; branching processes; limit theorems;
Markov chains in continuos time with emphasis on birth and death
processes and queueing theory. An introduction to Brownian motion, stationary processes and martingales. Cost: 3 WL: 3 (Belisle)

**531/Econ. 677. Analysis of Time Series. *** Stat.
426. (3). (Excl). *

The major topics include time and frequency- domain characteristics
of stationary discrete time series, autoegressive and moving average
models, prediction theory, estimation and hypothesis testing, and computer applications. Special topics might include vector
autoregression, cross-spectral analysis, causality testing or
other issues of current interest. Statistics 511 or Economics
775 is the standard prerequisite. Student evaluation is based
on exams, homework, and a term paper. Lectures and problem sets
including computer exercises are the main methods of instruction.
Cost:3 WL:3 (Howrey)

**550/SMS 576/I.O.E. 560. Bayesian Decision Analysis.
*** Stat. 425 or permission of instructor. (3). (Excl). *

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, icluding the general regression
model and contingency tables; Bayesian credible intervals and hypothesis tests; application to a variety of decision-making
situations. There will be assigned homework exercises, a midterm
and a final exam. Cost:3 WL:3 (Young)

**576/Econ. 776. Econometric Theory II. *** Econ.
775 or equivalent. (3). (Excl). *

This is a course in advanced econometrics. It includes a thorough
treatment of the general linear model, a development of simultaneous
equation techniques, and an introduction to nonlinear models.
Maximum likelihood and generalized method-of-moments estimators
are rigorously treated. Cost:3 WL:4 (Lee)

University of Michigan | College of LS&A | Student Academic Affairs | LS&A Bulletin Index

This page maintained by LS&A Academic Information and Publications, 1228 Angell Hall

The Regents
of the University of Michigan,

Ann Arbor, MI 48109 USA +1 734 764-1817

Trademarks of the University of Michigan may not be electronically or otherwise altered or separated from this document or used for any non-University purpose.