**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 other disciplines. Evaluation is based upon class examinations, a final examination, and weekly assignments. The course format is lecture with some discussion. (B. Hill, Hammerstrom)

**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. (Kuo)

**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). *

This course provides an analysis of engineering data associated with stochastic industrial processes. Topics include: fundamentals of distribution analysis; process model identification, estimation, testing of hypotheses, validation procedures, correlation and regression analysis, and quality control. Students are required to use the MTS computer system for problem solving. The course format includes three lectures and a lab. There are regularly assigned homework exercises, two term quizzes, and a final examination. Applications are mainly drawn from problems in engineering. (Kochhar)

**402. Introduction to Statistics and Data Analysis.
*** No credit granted to those who have completed 412.
(4). (NS). *

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 (1. 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. (Rothman, Starr and Staff)

**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, multicollinearity
and variable selection. Fixed, random, and mixed models are all
discussed in the analysis of variance.

**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 Econ. 404. (4). (SS). *

See Economics 405. (Kmenta)

**412. Introduction to Probability and Statistics. *** Prior
or concurrent enrollment in Math. 215 and either CCS 274 or Engin.
102. No credit granted to those who have completed 311 or 402.
(3). (NS). *

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. (Wong)

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

See Mathematics 425.

**500. Applied Statistics I. *** Math. 417 and a course in statistics (Stat. 402 or 426); or permission of instructor.
(3). (NS). *

Review of matrices, multivariate normal and related distributions. Regression and general least squares theory, Gauss-Markov theorem, estimation of regression coefficients, polynomial regression, step-wise regression, residuals. ANOVA models, multiple comparisons, analysis of covariance, Latin square designs, random and mixed-effect models. Applications and real data analysis will be stressed, with students using the computer to perform statistical analysis. (Wong)

**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.
510 for one credit. (3). (N. Excl). *

Statistics 510 covers basic topics in probability, including random variables, distributions, conditioning, independence, expectation and generating functions, special distributions and their relations, transformations, non-central distributions, the multivariate normal distribution, convergence concepts, and limit theorems. (Hoppe)

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

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. There will be assigned homework exercises, a midterm and a final examination. (Andrews)

**560/Biostat. 685 (Public Health). Introduction to Nonparametric
Statistics.*** Stat. 426 or permission of instructor.
(3). (NS). *

The course will cover the basic linear rank statistics for one sample, two sample, one and two way ANOVA, and regression problems. Tests and their power functions, point and interval estimates are covered. Efficiencies of rank and parametric tests are computed. (Hammerstrom)

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