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Courses

325 Survey Sampling
338 History of Statistics
344 Statistical Computing
345 Statistical Demography
350 Regression Analysis
351 Design and Analysis of Experiments
352 Nonparametric Statistical Methods
355 Analysis of Qualitative Data
359 Topics in Statistics
420-1,2,3 Introduction to Statistical Theory and Methodology
448 Multivariate Statistical Methods
453 Survival Analysis
454 Time-Series Analysis
455 Advanced Analysis of Qualitative Data
456 Generalized Linear Models (1)
457 Applied Bayesian Inference (1)
461 Advanced Topics in Statistics
466 Likelihood Methods (1)
499 Independent Study

325 Survey Sampling
Probability sampling, simple random sampling, error estimation, determination of sample size, stratification, systematic sampling, replication and pseudo- replication methods, ratio and regression estimation, cluster sampling, multiphase sampling, and nonsampling errors.

338 History of Statistics
Historical survey of the development of modern statistics, from Bernoulli’s law of large numbers to the contributions of R.A. Fisher. Prerequisite: IEMS 304 or equivalent344 Statistical Computing
Exploration of the theoretical and practical problems in the development and use of statistical computing systems for numerical and graphical analysis of data. Prerequisite: two quarter courses of STAT 350, 351; PSYCH 351; IEMS 404, 311; MATH 217; or equivalent.

345 Statistical Demography

Self contained introduction to statistical theory of demographic rates(births, deaths, migration) in multistate setting; statistical model underlying formal demography; analysis of error in demographic forecasting. Prerequisite: MATH 217; STAT 330; or equivalent

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350 Regression Analysis
Development of statistical techniques for linear regression, with an emphasis on applications to empirical data. Least- squares methods, confidence intervals, tests of hypotheses, measurement of association, and residual analysis. Criteria and methods of model selection. Computational and inferential procedures for nonlinear regression. Use of computer packages is emphasized throughout the course.

351 Design and Analysis of Experiments
Methods of designing experiments and analyzing data obtained from them: one- way and two-way layouts, incomplete block designs, Latin squares, Youden squares, factorial and fractional factorial designs, random-effects and mixed-effects models, and split-plot and nested designs.

352 Nonparametric Statistical Methods
A survey of nonparametric methods with emphasis on their theoretical rationale, basic properties, and typical applications. Topics include the sign, Mann-Whitney, Wilcoxon signed rank, rank correlation, Kruskal-Wallis, and Friedman tests.

355 Analysis of Qualitative Data
An introduction to the analysis of qualitative data with emphasis on the use of log-linear models. Topics include polytomous responses, two-way tables, multiway tables, logits, multinomial responses, incomplete tables, symmetric tables, adjustment techniques, and latent- class models.

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359 Topics in Statistics
Topics in theoretical and applied statistics, to be chosen by the instructor. This course may be taken more than once for credit.

420-1,2,3 Introduction to Statistical Theory and Methodology
This three-quarter sequence provides a comprehensive introduction to statistical theory and methodology. The course covers several major areas of statistical theory, including distribution theory, theory of estimation and hypothesis testing, large-sample theory, Bayesian methods, and decision theory. The emphasis is on those theoretical topics that are used in the development of statistical methods and the application of theoretical ideas to models used in practice. The normal-theory linear model will be considered in detail.

448 Multivariate Statistical Methods
Development of methods for analysis of multiple continuous responses. Emphasis on multivariate regression analysis and multivariate analysis of variance, with consideration of canonical correlation, discriminant analysis, and principal components. Use of standard computer packages is emphasized throughout the course.

453 Survival Analysis
This course deals with the modern methods used to analyze time-to-event data. Background theory is provided, but the emphasis is on using the methods and interpreting the results. The course covers of survivor functions, Kaplan-Meier curves, log-rank tests, Cox regression, model-fitting strategies, model interpretation, stratification, time-dependent covariates, and an introduction to parametric survival models.

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454 Time-Series Analysis
This course considers the analysis of time series using both Fourier analysis and ARIMA models. Topics in Fourier analysis include harmonic regression, power spectra, cross-spectra, and the study of linear filters. Topics in ARIMA models include problems of model selection and estimation.

455 Advanced Analysis of Qualitative Data
This course provides a general survey of statistical methods for qualitative data. Topics include log-linear models with fixed scores, multinomial response models, incomplete contingency tables, and symmetry models. The course examines alternatives to log-linear models such as probit and latent-class models and introduces adjustment methods based on log-linear models.

STAT 456 Generalized Linear Models (1)
Inference and fitting of generalized linear models with application to classical linear models, binomial and multinomial logit models, log-linear models, Cox’s proportional hazards model and GEE’s for longitudinal data. Prerequisites: STAT 350 and Stat 420-3

STAT 457 Applied Bayesian Inference (1)
Introduction to computational algorithms for Bayesian inference. Observed data and data augmentation methods are considered in detail. Methods are illustrated with real examples. Prerequisites: STAT 350 and STAT 420-1 or equivalent.

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461 Advanced Topics in Statistics
Topics in theoretical and applied statistics, to be chosen by the instructor.

STAT 466 Likelihood Methods (1)
Recent results in the theory of likelihood-based inference. Topics covered will include higher-order asymptotic theory, based both on Edgeworth expansion and saddlepoint methods, conditional and marginal likelihood functions, the modified profile likelihood function and adjustments to the signed likelihood ratio statistics. Prerequisites: STAT 420-2

499 Independent Study

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