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100. Introduction to Statistical Reasoning. 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).
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). Cost:2 WL:3 003: Gunderson)
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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 Econ. 405, or Stat. 265, 311, or 405. One credit granted to those who have completed Stat. 402. (3). (MSA). (BS).
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. Cost:2 WL:3 (Y. Wu)
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413. The General Linear Model and Its Applications. Stat. 402 and Math. 217; concurrent enrollment in Stat. 425. Students who have not taken Math. 217 should elect Stat. 403. Two credits granted to those who have completed Stat. 403. (4). (Excl). (BS).
Some motivating real examples - regression, ANOVA, time series - abstraction into a common model; statement of the model and assumptions; description of the design matrix including dummy variables; discussion of the error vector and assumptions regarding those errors; geometry of the GLM, including projections, Pythagorus, least squares estimation, residuals, predicted values, Gauss-Markov result, etc.; normal distribution theory results; confidence and predictive intervals, F and t tests, the extra sum of squares principle; multiple and partial correlations with geometry; checking for violations of the assumptions, normal probability plots, outliers, influence functions, problems of multicollinearity or near collinearity; cures for violations, transforms, weighting, etc. Multiple regression applications; choice of independent variables, principal components, all possible regressions, stepwise procedures, use of data subsamples (validation); polynomial regression, orthogonal polynomials. Use of dummy variables and ANOVA applications, fixed effect completely crossed ANOVA cases, balanced versus unbalanced designs, contrasts, interactions, multiple inference procedures including at least Scheffe, studentized range and Bonferroni; nested designs, etc. Time series applications, use of polynomial regression, deseasonalization, leads, lags and autoregressive models, serial correlation, Durbin-Watson test, ARIMA models, etc. Real applications will be stressed. There will be weekly assignments and a final exam. Class format is three hours of lecture and 1.5 hours of laboratory per week. Note: This course is designed primarily for Statistics Undergraduate Concentrators; other students, without the Mathematics 217 prerequisite, should elect Statistics 403. (Keener) Cost:2 WL:3
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425/Math. 425. Introduction to Probability. Math. 215, 255, or 285. (3). (MSA). (BS).
Sections 001 and 004 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. Cost:2 WL:3
Sections 003 and 005 See Mathematics 425.
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426. Introduction to Mathematical Statistics. Stat. 425. (3). (MSA). (BS).
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. Cost:2 or 3 WL:3 (Jeganathan)
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466/IOE 466/Manufacturing 466. Statistical Quality Control. Stat. 265 or 311. (3). (Excl). (BS).
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. Cost:2 WL:3 (Herrin)
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470. Experimental Design. Stat. 402. (4). (Excl). (BS).
This course will introduce students to basic principles in classical experimental design, including randomization, replication, confounding, interaction, covariates, use of the general linear model. Students will be introduced to the following designs: completely randomized, randomized blocks, Latin squares, incomplete blocks, factorial, split plot, Taguchi, response surface, optimal. There will be weekly assignments and a final exam. Class format is 3 hours of lecture and 1.5 hours of laboratory per week. Cost:2 WL:3 (Dass)
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500. Applied Statistics I. Math. 417, and Stat. 402 or 426. (3). (Excl). (BS).
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 squares, 2p factorial designs, random and mixed-effects models. Applications and real data analysis will be stressed, with students using a computer to perform statistical analyses. Cost:2 WL:3 (Faraway)
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505/Econ. 673. Econometric Analysis. Permission of instructor. (3). (Excl). (BS).
This course is designed for first-year graduate students in economics, business, and related subjects. It involves a fairly rigorous development of statistical reasoning and methods relating to hypothesis testing, estimation, and regression analysis. Students are supposed to have had a course in calculus and in introductory statistics. Knowledge of matrix algebra is required. Evaluation of students is based on midterm and final examinations and weekly assignments. Students taking this course are expected to take Economics 674 - Econometric Analysis II in the following term. Cost:2 WL:3 (Killian)
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510. Mathematical Statistics I. Math. 450 or 451, and a course in probability or statistics. (3). (Excl). (BS).
Review of probability theory including: probability, conditioning, independence, random variables, standard distributions, exponential families, inequalities, and the central limit theorem. Introduction to decision theory including: models, parameter spaces, decision rules, risk functions, Bayes versus classical approaches, admissibility, minimax rules, likelihood functions and sufficiency. Estimation theory including unbiasedness, complete sufficient statistics, Lehmann-Scheffe and Rao-Blackwell theorems, and various types of estimators. Cost:3 WL:3 (Keener)
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525/Math. 525. Probability Theory. Math. 450 or 451. Students with credit for Math. 425/Stat. 425 can elect Math. 525/Stat. 525 for only one credit. (3). (Excl). (BS).
See Mathematics 525.
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535. Reliability. Stat. 425 and 426. (3). (Excl).
This course will cover the important reliability concepts and methodology that arise in modeling, assessing and improving product reliability and in analyzing field and warranty data. Topics will be selected from the following: Basic reliability concepts; Common parametric models for component reliability; Censoring schemes; Analysis of time-to-failure data; Accelerated testing for reliability assessment; Modeling and analyzing repairable systems reliability; Analysis of warranty and field-failure data; Maintenance policies and availability; Reliability improvement through experimentation. Cost:2 or 3 WL:3
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550/SMS 576 (Business Administration)/IOE 560. Bayesian Decision Analysis. Stat. 425. (3). (Excl). (BS).
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 exam. Cost:3 WL:3
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