Statistics Graduate Courses
Course code is followed by course name and credits. In parentheses are shown the number of weekly lecture and laboratory hours respectively. Finally the semester for which the course is offered is shown. Unless otherwise indicated, these courses are offered each year.
STAT 602 - Experimental Design 3 Credit (3 + 0) Alternate Spring
Constructing and analyzing designs for experimental investigations; completely randomized, randomized complete block and Latin-square designs, split-plot designs, incomplete block designs, confounded factorial designs, nested designs, treatment of missing data, comparison of designs. (Prerequisites: STAT 401 or permission of instructor). Example text: Experimental Design by Douglas Montgomery
STAT 605 - Spatial Statistics 3 Credit (3 + 0) As Demand Warrants
Stochastic processes. Geostatistics including kriging and spatial design of experiments. Point processes including model selection and K-functions. Lattice process models and image analysis. Computer intensive statistical methods. (Prerequisites: STAT 401 and MATH 202 or permission of instructor.) Example text: Statistics for Spatial Data by Cressie.
STAT 611 - Time Series 3 Credit (3 + 0) As Demand Warrants
An applied course in time series and repeated measure analysis. Autoregression and moving average models. Estimation of parameters and tests. Prediction. Spectral analysis. (Prerequisites: STAT 401 or permission of instructor.) Example text: Time Series by Kendall and Ord.
STAT 621 - Distribution-Free Statistics 3 Credit (3 + 0) As Demand Warrants
Methods for distribution-free (non-parametric) statistical estimation and testing. These methods apply to many practical situations including small samples and non-Gaussian error structures. Univariate, bivariate, and multivariate tests will be presented and illustrated using a variety of applications and data sets. (Prerequisites: STAT 200 (Juneau STAT 373); STAT 401 recommended, or permission of instructor). Example text: Practical Nonparametric Statistics by Conover.
STAT 631 - Categorical Data Analysis 3 Credit (3 + 0) Alternate Fall
Statistical methods designed for count and categorical data. Contingency tables. Logistic and related models. Loglinear models. Repeated categorical responses. Survival data. (Prerequisites: STAT 401 or permission of instructor). Example text: Categorical Data Analysis by Agresti.
STAT 640 - Exploratory Data Analysis 3 Credit (3 + 0) As Demand Warrants
Quantitative and graphical methods for explaining data and for presenting data to others. Computer-aided detection and analysis of patterns in data. Methods for analysis of patterns in data. methods for validating the assumptions of common statistical tests and models. Use of computer graphics in statistical analysis. (Prerequisite: STAT 200 (Juneau STAT 373); STAT 401 recommended). Example text: Typically a text by Tukey.
STAT 651 - Statistical Theory I 3 Credit (3 + 0) Fall
Probability, distributions of random variables, conditional probability and stochastic independence, distributions of functions of random variables, expectation, limiting distributions, moment generating functions, distributions derived from the normal distributions. (Prerequisites: MATH 202, MATH 314, STAT 200, 300, or MATH 371, STAT 401 recommended.) Example text: Mathematical Statistics and Data Analysis by Rice.
STAT 652 - Statistical Theory II 3 Credit (3 + 0) Spring
Estimation of parameters including evaluation of efficiency and sufficiency, maximum likelihood and method of moments estimation, bootstrap and other resampling techniques to estimate variances, and construction of confidence intervals. Hypothesis testing including the Neyman-Pearson paradigm and likelihood ratio tests for evaluating one and two sample problems, the analysis of categorical data, and analysis of variance problems. Frequentist and Bayesian inference. (Prerequisites: STAT 651.) Example text: Mathematical Statistics and Data Analysis by Rice.
STAT 653 - Statistical Theory III 3 Credit (3 + 0) Fall
Best linear unbiased estimation, Gauss-Markov theory and applications, maximum likelihood estimation for linear models, multivariate normal distributions, linear regression and analysis of variance, weighted regression, robust and nonlinear regression, logistic regression, Poisson regression, ridge regression, smoothing, simple time domain models. (Prerequisites: STAT 652 or MATH 408 and STAT 401 and MATH 314.) Example text: An Introduction to Computational Statistics: Regression Analysis by Jennrich.
STAT 654 - Consulting Seminar 3 Credit (3 + 0) Spring
Topics related to recent consulting problems. Students will be involved in consulting for other graduate students and faculty and in learning about consulting practices. (Prerequisite: admission into the interdisciplinary graduate program in statistics). May be repeated for credit up to 3 credits. Example text: none.
STAT 661 - Sampling Theory 3 Credit (3 + 0) Alternate Spring
Statistical theory for sampling and sample surveys. Choice of method, power, and sample size considerations, treatment of sampling and non-sampling biases. Sampling methods based on detectability. Adaptive sampling. Spatial sampling. Mark and recapture methods. The jackknife, the bootstrap, and resampling plans. (Prerequisites: STAT 200 (Juneau STAT 373); STAT 401 recommended, or permission of instructor). Example text: Sampling by Thompson.
STAT 680 - Data Analysis in Biology 3 Credit (2 + 3) As Demand Warrants
Biological applications of nonparametric statistics, including tests based on binomial and Poisson distributions, analysis of two-way and multiway contingency tables, and tests based on ranks; multivariate statistics, including principle component analysis ordination techniques, cluster analysis, and discriminate analysis; and time-series analyses. Introduction to the use of the computer, and use of statistical packages. Each student will analyze a data set appropriate to the student's research interests. (Prerequisites: STAT 300, 401 and either graduate standing in a biologically oriented field or permission of instructor). Example text: none.