Dept. of Statistics Spring & Summer 2008 Course Schedules

Note:

• Special Needs: "Any students with disabilities or other special needs, who need special accommodations in the course, are invited to share these concerns or requests with the instructoras soon as possible."
• Academic Honesty: All work in this course must be completed in a manner consistent with NDSU University Senate Policy, Section 335: Code of Academic Responsibility and Conduct.

Frequency tables, histograms, probability, wellknown 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 lemma, 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.

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.
NOTE: 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 for knowledge discovery from biological data.
Prereq: STAT 461/661.
Cross-listed with MATH 735 and CSci 732.

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.

Multiple regression, analysis of residuals, model building, regression diagnostics, multi-collinearity, robust regression, and nonlinear regression.
Prereq: STAT 468/768, 461/661, course in matrix algebra.

One-way classification models with heterogeneous errors. 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.

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:
690, 790 Seminar 1-3
696, 796 Special Topics 1-5
793 Individual Study 1-5
794 Consulting/Presentation Practicum 1
797 Master's Paper 1-3
797R Paper Continuing Registration 1
798 Master's Thesis 1-6
798R Thesis Continuing Registration 1
799 Doctoral Dissertation 1-15
799R Dissertation Continuing Registration