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
Top
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.
Top
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.
Top
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.
Top
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
