Course Offerings
330 Introductory Statistics / 3 Credits
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)
331 Regression Analysis / 2 Credits
Simple and multiple regression techniques and correlation coefficients. Extensive use of SAS. Emphasis on applications.
Prereq: STAT 330
367 Probability / 3 Credits
Probability, probability distributions for discrete random variables, probability density functions, marginal joint probability density functions, expected value and variance, and transformations.
Prereq: MATH 147 or 166
368 Statistics / 3 Credits
Moments, moment generating functions, central limit theorem, one and two sample tests of hypotheses, estimation, and simple linear regression and correlation.
Prereq: STAT 367
450/650 Stochastic Processes / 3 Credits
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
451/651 Bayesian Statistical Decision Theory / 3 Credits
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
460/660 Applied Survey Sampling / 3 Credits
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
461/661 Applied Regression Models / 3 Credits
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
462/662 Introduction to Experimental Design / 3 Credits
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
463/663 Nonparametric Statistics / 3 Credits
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
464/664 Discrete Data Analysis / 3 Credits
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
465/665 Meta-Analysis Methods / 3 Credits
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
467 Probability and Mathematical Statistics I / 3 Credits
Random variables, discrete probability distributions, density functions, joint and marginal density functions, transformations, limiting distributions, central limit theorem.
Prereq: MATH 265 or STAT 368
468 Probability and Mathematical Statistics II / 3 Credits
Properties of estimators, confidence intervals, hypotheses testing, Neyman-Pearson Iemma, likelihood ratio tests, complete and sufficient statistics.
Prereq: STAT 467
469/669 Introduction to Biostatistics / 3 Credits
Introduction to biostatistical concepts and reasoning. Inference on means and proportion; hypothesis testing; group comparisons; nonparametric methods; sample size estimation; contingency tables; simple and multiple regression; logistic regression.
Prereq: STAT 330
470/670 Statistical SAS Programming / 3 Credits
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
471/671 Introduction to the R Language / 3 Credits
Syntax and capabilities of the R language. R commands, expressions and matrix operations. Operations with internal built-in as well as user-written functions. Programming in R as well as optimization and graphical capabilities. Application of R to statistical problem solving; this includes linear and nonlinear regression, experimental design, and several other problems.
Prereq: STAT 367 and STAT 368
472/672 Time Series / 3 Credits
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
476 Actuary Exam Study II / 1 Credit
Selected material from probability and mathematical statistics in preparation for the national actuarial exam.
Prereq: STAT 368 or 468
477/677 Introductory Survival and Risk Analysis I / 3 Credits
Survival distributions, life tables, and various risk models, intended to prepare students for taking higher level actuarial exams: SOA1 Course FM/CAS2 Exam 2 and SOA Course MLC/CAS Exam 3L.
Prereq: STAT 367 or STAT 467
478/678 Introductory Survival and Risk Analysis II / 3 Credits
Distribution of the random variable-the time until future of a joint-life status, life tables, completing risks and multiple decrement probabilities, Markov chain and Poisson models, intended to prepare students for taking the actuarial exams: SOA1 Course MLC/CAS2 Exam 3L.
Prereq: STAT 477 or STAT 677
725 Applied Statistics / 3 Credits
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.
726 Applied Regression and Analysis of Variance / 3 Credits
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.
730 Biostatistics / 3 Credits
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
732 Introduction to Bioinformatics / 3 Credits
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
761 Advanced Regression / 3 Credits
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
762 Messy Data Analysis / 3 Credits
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
764 Multivariate Methods / 3 Credits
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
767 Probability and Mathematical Statistics I / 3 Credits
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
768 Probability and Mathematical Statistics II / 3 Credits
Properties of estimators, confidence intervals, hypotheses testing. Neyman-Pearson Lemma, likelihood ratio tests, complete and sufficient statistics. Additional project required.
Prereq: STAT 767
770 Survival Analysis / 3 Credits
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
772 Computational Statistics / 3 Credits
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
774 Linear Models I / 3 Credits
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
777 Multivariate Theory / 3 Credits
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
778 Modern Probability Theory
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
780 Asymptotics, Bootstrap, and Other Resampling Plans / 3 Credits
Development of large sample and small sample properties of a variety of estimators.
Prereq: STAT 768
786 Advanced Inference / 3 Credits
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 Credits
696, 796 Special Topics / 1-5 Credits
793 Individual Study / 1-5 Credits
794 Consulting/Presentation Practicum / 1 Credit
797 Master's Paper / 1-3 Credits
797R Paper Continuing Registration / 1 Credit
798 Master's Thesis / 1-6 Credits
798R Theses Continuing Registration / 1 Credit
799 Doctoral Dissertation / 1-15 Credits
799R Dissertation Continuing Registration / 1 Credit
- 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 instructors as 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.
