Frequency tables, histograms, probability, well-known
probability distributions, one and two sample tests of hypotheses,
confidence intervals, and contingency tables.
Prereq: MATH 103, 104, or 107. (ND: MATH)
Simple and multiple regression techniques and correlation
coefficients. Extensive use of SAS. Emphasis on
applications.
Prereq: STAT 330
Probability, probability distributions for discrete random
variables, probability density functions, marginal joint
probability density functions, expected value and variance, and
transformations.
Prereq: MATH 166
Moments, moment generating functions, central limit theorem, one
and two sample tests of hypotheses, estimation, and simple linear
regression and correlation.
Prereq: STAT 367
Discrete time Markov chains, Poisson processes, continuous time
Markov chains, birth and death processes, renewal processes,
branching processes, queuing systems, and applications.
Prereq: STAT 368
Bayesian approach to statistics including utility and loss,
prior and posterior densities, and Bayesian inference. Comparisons
with classical statistical methods.
Prereq: STAT 368 or 468
Simple random, stratified, systematic and cluster sampling;
two-stage sampling. Estimation of population means and variances.
Ratio and regression estimators.
Prereq: STAT 330 or 368
Simple linear regression, matrix approach to multiple
regression, and introduction to various tests and confidence
intervals. Includes discussion of multicollinearity and
transformations.
Prereq: STAT 330 or 368, knowledge of matrix algebra
Fundamental principles of designing an experiment, randomized
block, Latin square, and factorial. Also covers analysis of
covariance and response surface methodology.
Prereq: STAT 330 or 368
Various tests and confidence intervals that may be used when the
underlying probability distributions are unknown, including the
Wilcoxon, Kruskal-Wallis, and Friedman.
Prereq: STAT 330 or 368
Application of binomial, hypergeometric, Poisson, mixed Poisson,
and multinomial distributions in discrete data analysis.
Log-linear models and contingency tables. Logistic regression.
Discrete discriminant analysis.
Prereq: STAT 368
Statistical methods for meta-analysis with applications.
Various parametric effect size from a series of experiments: fixed
effect, random effect linear models; combining estimates of
correlation coefficients; meta-analysis in the physical and
biological sciences.
Prereq: STAT 331, 461/661 or 725
Random variables, discrete probability distributions, density
functions, joint and marginal density functions, transformations,
limiting distributions, central limit theorem.
Prereq: MATH 265 or STAT 368
Properties of estimators, confidence intervals, hypotheses
testing, Neyman-Pearson Iemma, likelihood ratio tests, complete and
sufficient statistics.
Prereq: STAT 467
Focuses on statistical problem solving and writing SAS computer
code. Data types, data management, data input/output, SAS as a
programming language, data analysis, report writing and
graphing.
Prereq: STAT 461/661, 462/662, or 726
Estimation of trend in time series data. Seasonal models.
Stationary models. Moving average, autoregressive and ARMA models.
Model identification. Forecasting. Intervention analysis.
Prereq: STAT 468/768, 461/661, course in matrix algebra
Selected material from probability and mathematical statistics
in preparation for the national actuarial exam.
Prereq: STAT 368 or 468
Data description, probability, inference on means, proportions,
difference of means and proportions, categorical data, regression,
analysis of variance and multiple comparisons.
Prereq: Knowledge of algebra
This course is not intended for statistics or
mathematics majors.
Simple and multiple regression, ANOVA tables, correlation,
regression diagnostics, selection procedures, analysis of
covariance, one-way ANOVA, two-way ANOVA.
Prereq: STAT 725
This course is not intended for statistics or
mathematics majors.
Direct assays, parallel line assays, slope ratio assays,
multiple assays, and quantal assays. Model, estimation and
testing. Probit and logit analysis.
Prereq: STAT 461/661 or 725
An introduction to the principles of bioinformatics including
statistical techniques for the analysis of one or more gene
sequences and computational techniques of knowledge discovery from
biological data.
Prereq: STAT 461/661
Cross-listed with MATH 735 and CSCI 732
Multiple regression, analysis of residuals, model building,
regression diagnostics, multicollinearity, robust regression, and
nonlinear regression.
Prereq: STAT 468/768, 461/661, course in matrix algebra
One-way classification models with heterogeneous error. Two-way
classification analysis in the unbalanced case. Analysis of mixed
models. Split-plot, nested and crossover designs.
Prereq: STAT 462/662
Sample geometry, correlation, multiple, partial, canonical
correlation test of hypothesis on means, multivariate analysis of
variance, principal components, factor analysis, and discriminant
analysis.
Prereq: STAT 461/661 or 462/662, course in matrix algebra
Random variables, discrete probability distributions, density
functions, joint and marginal density functions, transformations,
limiting distributions, central limit theorem. Additional project
required.
Prereq: MATH 265 or STAT 368
Properties of estimators, confidence intervals, hypotheses
testing. Neyman-Pearson Lemma, likelihood ratio tests, complete
and sufficient statistics. Additional project required.
Prereq: STAT 767
Presents basic methodology in the analysis of censored data, two
basic types of censoring, parametric estimation, nonparametric
estimation, and life table methods.
Prereq: STAT 768
Assortment of computational statistics and statistical computing
techniques. Specific topics include: random variable generation,
optimization and root fining, resampling statistics, Monte Carlo
methods, statistical graphics, non-linear and generalized least
squares, and the EM algorithm.
Prereq: STAT 661 and STAT 768
General linear models. Full rank models. Estimation,
confidence ellipsoids and tests of hypotheses. Not full rank
models. Applications to regression and design of experiments.
Prereq: STAT 768, course in matrix algebra
Repeated measurements models. Variance components models.
Response surfaces. Growth curve models, unbalanced designs.
Prereq: STAT 774
Wishart distribution, distribution of Hotelling's T-square and
Lambda statistics, cluster analysis, correspondence analysis,
principal components, factor analysis, discriminant analysis,
multidimensional scaling.
Prereq: Stat 764
Probability theory presented from the measure theoretic
perspective. Emphasis on various types of convergence and limit
theorems. Discussion of random walks, conditional expectations,
and martingales.
Prereq: STAT 768 or MATH 750
Cross-listed with MATH
Development of large sample and small sample properties of a
variety of estimators.
Prereq: STAT 768
Further discussion of properties of estimators, theory of
estimation, and hypotheses testing.
Prereq: STAT 768
The following variable credit courses are also offered: