**300. Introduction to Statistical Reasoning. *** (3).
(NS). *

This course is designed to provide an overview of the field of statistics. Course topics include approaches to the collection of numerical data, methods of analyzing and summarizing such 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. Applications are drawn from a wide variety of disciplines. Evaluation is based upon class examinations, a final examination, and weekly assignments. The course format is lecture with some discussion. (Section 001 – Ericson; Section 002 – Keener)

**310. Elements of Probability. *** Prior or
concurrent enrollment in Math. 215. (3). (NS). *

This course covers the main ideas and uses of probability: expectation, variance, covariance, distribution functions, bivariate, marginal and conditional distributions, the binomial and related distributions, the Poisson process, the exponential and gamma distributions, the normal sample statistics, the law of large numbers, the central limit theorem. There are regularly assigned homework exercises, two in-class examinations, and a final examination. The emphasis is on problem solving and applications. (Donnelly)

**311/I.O.E. 365. Engineering Statistics. *** Math.
215 and Eng. 102, or equivalent. No credit granted to those who
have completed 412. (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. (Hoppi)

**402. Introduction to Statistics and Data Analysis.
*** No credit granted to those who have completed 412.
(4). (NS).
Section 00l. * 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. The course format includes three lectures and a laboratory
(l.5 hours per week). The laboratory section covers some of the
data analysis material and introduces the use of interactive computing through the use of MIDAS. Course evaluation is based on a combination
of three examinations

*Section 002. * This course is designed for students
with an interest in the application of the scientific method and in the use of Michigan Interactive Data Analysis System (MIDAS).
Statistics 402 has no prerequisite and has been elected by many
students whose mathematics background includes only high school
algebra. The course is "applications oriented" and is
appropriate for students from all academic areas. The course focuses
on the general problems associated with conclusions drawn on the
basis of observation. Examples which reflect student interests
are chosen, and all concepts are illustrated * via * these
examples. The course format includes three lectures and a laboratory
(l.5 hours) each week. The laboratory introduces the use of MIDAS
and serves as a recitation section. Course evaluation is based
upon a combination of class examinations, a midterm, a final, and class discussion. (Lenk)

**403. Introduction to Statistics and Data Analysis II.
*** Stat. 402. (4). (NS).
Applied Regression and Analysis of Variance. * This course
surveys some intermediate topics in multiple linear regression
and the analysis of variance and covariance, stressing applications
rather than theory. We particularly emphasize residual analysis
in multiple regression and cover such topics as the least squares
estimation and tests of hypotheses, prediction analysis, multicolinearity
and variable selection. Fixed, random, and mixed models are all
discussed in the analysis of variance.

**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. (Muirhead)

**526/Math. 526/C.I.C.E. 516.
Discrete State Stochastic Processes. *** Math. 525; or
Stat. 510; or C.I.C.E. 512. (3). (N.Excl). *

Generating functions; recurrent events and the renewal theorem; random walks; Markov chains; branching processes; limit theorems; Markov chains in continuous time with emphasis on birth and death processes and queueing theory. An introduction to Brownian motion, stationary processes, or martingales.

**531. Statistical Analysis of Time Series. *** Stat.
426. (3). (NS). *

The major topics include time- and frequency-domain characteristics of stationary discrete time series, autoregressive 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. (Howrey)

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

This is a course in advanced econometrics. It includes a thorough treatment of statistical problems in dealing with time series and cross-section data, a development of simultaneous equation techniques, and formulation and estimation of special models. Other topics may also be covered.

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