136 / FIELDS OF INSTRUCTION


STA 6166-Statistical Methods in Research I (4) Statistical in-
ference based on t, F, and X2 tests. Analysis of variance for basic
experimental designs. Factorial experiments. Regression analysis
and analysis of covariance.
STA 6167-Statistical Methods in Research II (4) Prereq: STA
6166. Analysis of split-plot and nested designs with incomplete
blocks, confounding and fractional replications. Analysis of
count data. Nonparametric methods.
STA 6176-Introduction to Biostatistics (3) Prereq: STA 6207,
6327. Definitions, terminology and research design concepts,
basic demographic concepts, measures of morbidity and
mortality, analysis of epidemiological studies and clinical trials,
introduction to survival analysis.
STA 6177-Advanced Topics in Biostatistics (3) Prereq: STA
6176. Analysis of biological assays, logit and probit analysis, non-
linear regression, survivorship analysis and competing risks
analysis, advanced methods for clinical trials.
STA 6200-Fundamentals of Research Design (2) Choosing the
research objective, determining the type of data to collect,
repeated measures and blocking; choosing the sample and the
randomization technique, designing a data collection form.
Applications to biomedical data.
STA 6201--Analysis of Research Data (3) Prereq: STA 6010.
Introduction to the most commonly used statistical analyses for
evaluating research data, with application to the biomedical
sciences. Emphasis on choosing the appropriate procedure and
evaluating the results properly, rather than on the computational
aspects of the procedures.
STA 6207-Design and Analysis of Experiments I (3) Prereq:
STA 4322. Basic concepts of experimental design. Principles of
statistical inference for linear models. Models of least squares,
regression analysis, analysis of variance. Factorial and nested
experiments, split plots. Analysis of covariance.
STA 6208-Design and Analysis of Experiments II (3)Prereq:
STA 6207. Multiple comparison procedures, incomplete blocks,
fractional factorials and confounding. Cross-over designs, and
control of residual effects.
STA 6226-Sampling Theory and Applications (3) Prereq: STA
6327 or consent of instructor. Theory and applicatioQ of
commonly used sampling techniques; simple random sample,
cluster, ratio, regression, stratified, multistage, and systematic
samples. Special topics include wildlife surveys, non-sampling
error adjustment, categorical data analysis, and practical survey
examples.
STA 6246-Theory of Least Squares (3) Prereq: STA 6207.
Theory underlying general linear models, parameter estimation,
and hypothesis testing in full rank and less than full rank models,
use of residuals in regression analysis, ill-conditioned problems,
biased estimation, variable screening procedures.
STA 6247-Advanced Topics in Design and Analysis (3) Prereq:
STA 6246, STA 6207-6208. General rules of analysis of variance
pertaining to balanced linear classification models. Procedures
of estimation and hypothesis testing for unbalanced data. Estima-
tion of variance components. Response surfaces for first and
second order linear models. Biased estimation procedure and
mixture experiments.
STA 6267-Sequential Methods (3) Prereq: STA 6327. Double
sampling procedures, the sequential probability ratio tests,
sequential tests for composite hypotheses, sequential estima-
tion and confidence intervals.
STA 6326-Introduction to Theoretical Statistics I (3) Prereq:
MAC 3314. Theory of probability. Probability spaces, continuous
and discrete distributions, functions of random variables,
multivariate distributions, expectation, conditional expectation,
central limit theorem, useful convergence results, sampling
distributions, distributions of order statistics, empirical distribu-
tion function.
STA 6327-Introduction to Theoretical Statistics II (3) Prereq:
STA 6326. Estimation and hypothesis testing. Sufficiency,
information, estimation, maximum likelihood, confidence
intervals, uniformly most powerful tests, likelihood ratio tests,
sequential testing, univariate normal inference, decision theory,
analysis of categorical data.
STA 6466-Probability Theory I (4) Prereq: MAA 5226 or
equivalent. Measure and probability spaces; random variables;


distribution functions; abstract Lebesque and Lebesque-Stieltjes
integration; monotone, dominated, Cauchy, and mean con-
vergence; Fubini and Radon-Nikodym theorems, zero-one laws.
STA 6467-Probability Theory II (4) Prereq: STA 6466. Sum-
mability of independent random variables, laws of large
numbers, convergence in distribution, characteristic functions,
uniqueness and continuity theorems, the Lindeberg-Feller cen-
tral limit theorem, degenerate convergence criterion.
STA 6505-Analysis of Categorical Data (3) Prereq: STA 6327
and STA 6207 or consent of instructor. Varieties of categorical
data, cross-classification tables, tests for independence. Measures
of association. Loglinear models for multi-dimensional tables.
Logit models and analogies with regression. Specialized methods
for ordinal data.
STA 6526-Nonparametric Statistics (3) Prereq: STA 6327 or
consent of instructor. Inference based on rank statistics-one,
twb and k-sample problems, correlation and regression problems
and analysis of contingency tables. Conditionally distribution-
free rank tests. Pitman asymptotic relative efficiency.
STA 6706-Applied Multivariate Methods for Behavioral
Research (3) Prereq: STA 6166 or consent of instructor. Bivariate
relationship: matrix algebra; review of multiple regression and
correlation; part and partial correlation; canonical correlation;
discriminant analysis and classification; cluster analysis; factor
analysis.
STA 6707-Analysis of Multivariate Data (3) Prereq: STA 6167
or 6208. Standard methodology in processing multivariate data
including an introduction to the following topics; cluster analysis,
discriminant analysis, factor analysis and principal components.
Classical tests on the mean and dispersion matrices are given.
STA 6746-Multivariate Analysis (4) Prereq: STA 6246. The
multivariate normal, Wishart and generalized beta distributions.
Likelihood tests of hypothesis concerning the mean and disper-
sion matrices. The u-statistitic.
STA 6826-Stochastic Processes I (3) Prereq: STA 6327. Discrete
time and state Markov process. Ergodic theory.
STA 6827-Stochastic Processes II (4) Prereq: STA 6826.
Continuous time Markov processes. The Poisson and allied
processes. The Kolmogorov equations. Renewal theory.
STA 6857-Applied Time Series Analysis (3) Prereq: STA 4322
and a basic computer language. Linear time series model
building, spectral density estimation, analysis of nonstationary
data, SAS package on Box and Jenkins model building and
forecasting. Case studies in recent literature will be discussed.
STA 6876-Theory of Time Series (3) Prereq: STA 6327. Foun-
dations of stationary time series, distributions of sample auto-
correlations, partial autocorrelation, spectral density, time series
regression, and special topics in recent time series research.
STA 6900-Problems in Statistics (1-4; max: 6) Prereq: per-
mission of department. Special problems in research methods,
sampling methods, and experimental designs.
STA 6905-Individual Work (1-4; max: 10) Prereq: permission
of department. Special topics designed to meet the needs and
interests of individual students.
STA 6910-Supervised Research (1-5) S/U.
STA 6934-Special Topics in Statistics (1-3; max: 8) Prereq:
permission of graduate adviser.
STA 6937-Seminar: Current Topics in Statistics (1-3; max: 6)
Prereq: permission of department. Discussion of current research
topics in statistics not covered in regular courses. S/U.
STA 6938-Seminar (1) Prereq: permission of department.
Special topics of an advanced nature suitable for seminar
treatment but not given in regular courses. S/U.
STA 6940-Supervised Teaching (1-5) S/U.
STA 6971-Research for Master's Thesis (1-15) S/U.
STA 7346-Statistical Inference I (4) Prereq: STA 6327. Decision
rules and risk functions. Sufficiency, Minimax, and Bayes rules
for estimation of location and scale parameters.
STA 7347-Statistical Inference II (4) Prereq: STA 7346.
Bayesian statistical inference. Inference using large samples.
Relative efficiencies of tests and estimates with special reference
to Pitman and Bahadur efficiencies.
STA 7980-Research for Doctoral Dissertation (1-15) S/U.