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.
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 given Wednesday evenings, a final examination and teaching fellow input. (Rothman)
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. Experimental designs studied include randomized complete block, hierarchial or nested designs and the latin square. Three hours of lecture and one and one-half hours of lab per week. (K. Smith)
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.
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
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.