**1000 Level | 2000 Level | 3000 Level | 4000 Level | 5000 Level**

USP Codes are listed in brackets by the 2003 USP code followed by the 2015 USP code (i.e. [QB<>Q]).

**JumpLink1101. First-Year Seminar. 3. [{none}<>FYS]**

**JumpLink2000. Statistics and the World. 3. [QB<>Q]** Discusses statistical reasoning and methods as related to today's society. Emphasizes ideas rather than specific techniques. Focuses on real examples of the use (and misuse) of statistics. Includes sampling, experimentation, descriptive statistics, elementary probability and statistical inference. *Prerequisite:* grade of C or better in MATH 0921 or level 2 on the Math Placement Exam or Math ACT of 21 or Math SAT of 600 or concurrent enrollment in MATH 1080.

**2050. Fundamentals of Statistics. 4. [QB<>Q]** Presents central ideas and fundamental techniques of statistical inference on applications in the biological sciences. Includes probability models and inferences for means, variances and parameters of discrete distributions. Introduces statistical computer packages in biweekly labs. Credit cannot be earned in more than one of the following courses: STAT 2010, 2050, 2070, 4220 and 5000. *Prerequisite:* grade of C or better in MATH 1000, 1400 or equivalent.

**2070. Introductory Statistics for the Social Sciences. 4. [QB<>Q]** Presents central ideas of descriptive statistics and statistical inference, as applied to questions in social sciences. Includes graphs, averages, sampling, estimation, hypothesis-testing and relationships between variables. Introduces associated computer skills. Credit cannot be earned in more than one of STAT 2010, 2050, 2070, 4220, 5000. Cross listed with SOC 2070. *Prerequisite:* grade of C or better in MATH 1000, 1400 or equivalent.

**2110. Statistical Methods for Business and Management Science. 3. **Provides majors in various departments of the College of Business with training in use of statistical analysis techniques as they apply to business problems. Credit cannot be earned in more than one of the following courses: STAT 2110, 3050 and 5050/5060/5070/5080. *Prerequisite:* STAT 2010 or consent of instructor.

**JumpLink3050. Statistical Methods - General. 3. **Provides undergraduate majors in the Colleges of Arts and Sciences, Agriculture and Education with training in statistical methodology for multiple variable situations. Integrates computer analysis packages such as R MINITAB, SAS and SPSSX into statistical topics. Credit cannot be earned in more than one of the following courses: STAT 2110, 3050 and 5050, 5060, 5070, 5080. *Prerequisite:* STAT 2050, 2070 or equivalent.

**JumpLink4015 [4010, 4410]. Regression Analysis. 3.** Contains standard topics, as well as some newer and more unconventional ones. Oriented towards analysts who use computer packages for problem solutions. Includes balance of application and theory. Dual listed with STAT 5015. *Prerequisite:* STAT 3050 or equivalent.

**4025 [4020, 4310]. Design and Analysis of Experiments I. 3.** Reviews design and analysis of one-factor experiments and introduces multi-factor experiments, Latin squares, nested designs and random effects. Includes topics such as polynomial response curves, trend analysis, split plots and incomplete blocks as time permits. *Prerequisite:* STAT 3050 or equivalent.

**4045 [4040]. Categorical Data Analysis. 3.** Applied methods for analyzing associations when some or all variables are measured in discrete categories, not continuous scales. Topics include the binomial, multinomial, and Poisson probability models, parameter estimation and hypothesis-testing about proportions, measures of association and tests for contingency tables, logistic regression, and log-linear models. Dual listed with STAT 5045. *Prerequisite:*: STAT 2110, 3050, 5050, 5060, 5070, or 5080.

**4070. Causal Models. 3.** Applications of least-squares and iterative maximum-likelihood methods for drawing cause and effect conclusions from nonexperimental data. Topics include regression-based path analysis, reciprocal causation, confirmatory factor analysis, measurement error, and structural equation models with unmeasured (latent) variables. Cross listed with SOC 4070. *Prerequisite:* one of STAT 3050, 4015, 5050, 5060, 5070, 5080 or equivalent (regression methods).

**4115 [4110]. Time Series Analysis and Forecasting. (B) 3.** An applied introduction to time series and forecasting. Brief coverage of time series regression, decomposition methods, and smoothing will lead into a more detailed coverage of Box-Jenkins (ARIMA) modeling. Computer analyses using MINITAB and SAS will be an important part of the course. Cross listed with ECON 4110; dual listed with STAT 5115. *Prerequisite:* STAT 3050 or equivalent; STAT 4015/5015 recommended.

**4155 [4150]. Fundamentals of Sampling. 3.** Develops methodology of simple random sampling, stratified sampling, and multistage sampling. Provides applications related to physical, social, and biological sciences. Discusses single and two-variable estimation techniques. Presents estimation based on subsamples from subpopulations. Dual listed with STAT 5155. *Prerequisite:* choice of STAT 2010, 2050, 2070 or equivalent.

**4220 [4020]. Basic Engineering Statistics. 3. **Introduces probability models, properties of distributions, statistical inference and development of statistical models for physical and engineering sciences. Credit cannot be earned in more than one of the following courses: STAT 2010, 2050, 2070, 4220 and 5000. *Prerequisite:* MATH 2205 or 2355 (or equivalent).

**4240. Data Mining. 3.** An introduction to statistical learning and data mining using techniques that have proven useful in recognizing patterns and making predictions. These techniques include both parametric and nonparametric models. Tools for computing and evaluating these techniques will also be studied. Dual listed with STAT 5240. *Prerequisite:* STAT 4015.

**4255 [4250]. Mathematical Theory of Probability. 3. **Calculus-based. Introduces mathematical properties of random variables. Includes discrete and continuous probability distributions, independence and conditional probability, mathematical expectation, multivariate distributions and properties of normal probability law. Dual listed with STAT 5255; cross listed with MATH 4255. *Prerequisite:* grade of C or better in MATH 2210. (Offered fall semester)

**4265 [4260, 4010]. Introduction to the Theory of Statistics. 3.** Presents derivations of theoretical and sampling distributions. Introduces theory of estimation and hypothesis testing. Dual listed with STAT 5265; cross listed with MATH 4265. *Prerequisite:* STAT/MATH 4255.

**4270. Applied Bayesian Statistics. 3.** This course introduces Bayesian data analysis in an applied context. We will learn about Bayesian statistics primarily in a regression model context, taken broadly. A conceptual understanding of popular Markov Chain Monte Carlo algorithms will be provided. Dual listed with STAT 5270. *Prerequisite:* STAT 3050. STAT 4015/5015 recommended.

**4280. Models for Hierarchical Data. 3.** Provides an introduction to the modeling and analysis of correlated/hierarchical data from exponential family member distributions (i.e. presence/absence, count data, Gaussian data). Emphasis is on applications. Aimed to build off of a first course in regression analysis. Dual listed with STAT 5280. *Prerequisite:* STAT 4015.

**4300. Applied Multivariate Analysis. 3.** The application of multivariate statistical methods in behavioral science research. Topics include: multivariate regression, canonical correlation, discriminate analysis, factor analysis and multidimensional scaling. A wide range of computer assistance is incorporated. Dual listed with STAT 5300. *Prerequisite:* STAT 3050 or equivalent.

**4350. Survey Construction and Analysis. 3.** Designed to examine the issues surrounding the construction (item wording, test theory, and numerical scales), assessment (sampling and psychometrics), and analysis (item analysis, qualitative data analysis, and factor analysis) of survey instruments. Roughly a third of the course is devoted to each of these areas. Dual listed with STAT 5350. *Prerequisite:* STAT 3050.

**4360. Spatial Statistics. 3.** Emphasis is on a generalized linear model approach to the modeling of continuous data, placing model building and the various kriging methods into a single conceptual framework. Dual listed with STAT 5360. *Prerequisite:* STAT 4015.

**4370. Survival Analysis. 3.** Introduction to the modeling of time to event data as it arises in epidemiological and medical research. Topics include parametric and non-parametric estimation for censored data without covariates, and for data with covariates, the proportional hazards regression model, additive hazards regression model and parametric regression models. Dual listed with STAT 5370. *Prerequisites:* STAT 4015 and 4025.

**4460. Statistical Software [5480]. 1.** An introduction to the various statistical software programs currently in use at the University of Wyoming. Topics will include the structure of each language, I/O, programming the basic statistical applications, and a comparison of the other languages. *Prerequisite:* 9 hours in statistics beyond introductory.

**4870. Senior Thesis. 3. **Encompasses senior thesis research project under faculty member guidance and supervision. Faculty sponsorship must be obtained prior to registration. *Prerequisites:* 18 hours in statistics and senior standing.

**4880 [4790]. Problems in Statistics. 1-4 (Max. 9).** Encourages individual initiative on part of students who work on extending their knowledge through library research. *Prerequisites:* senior standing, 8 hours in statistics and consent of instructor.

**JumpLink50****15. Regression Analysis. 3.** Contains standard topics, as well as some newer and more unconventional ones. Oriented towards analysts who use computer packages for problem solutions. Includes balance of application and theory. Dual listed with STAT 4015. *Prerequisite:* STAT 3050 or equivalent.

**5025. Design and Analysis of Experiments I. 3.** Reviews design and analysis of one-factor experiments and introduces multifactor experiments, Latin squares, nested designs, and random effects. Includes topics such as polynomial response curves, trend analysis, split plots and incomplete blocks as time permits. Dual listed with STAT 4025. *Prerequisite:* STAT 3050 or equivalent.

**5045. Categorical Data Analysis. 3.** Applied methods for analyzing associations when some or all variables are measured in discrete categories, not continuous scales. Topics include the binomial, multinomial, and Poisson probability models, parameter estimation and hypothesis-testing about proportions, measures of association and tests for contingency tables, logistic regression, and log-linear models. Dual listed with STAT 4045. *Prerequisite:* two courses in statistics.

**5050. Statistical Methods for the Biological Science. 3.** General statistical analyses and their application to the biological and behavioral sciences. Analysis of variance, regression and correlation methods are studied from a data analytic perspective, emphasizing the conceptual understanding of where and when these techniques should be used and the interpretation of their results. Available computer programs will be utilized. Credit cannot be earned in more than one of the following courses: STAT 3050, 5050, 5060, 5070. Cross listed with ZOO 5050. *Prerequisite:* one course in statistics (all introductory courses except 2000).

**5055. Statistical Methods for the Biological Sciences II. 3.** The statistical toolkit (regression and ANOVA-driven) of methods applicable to the biological and behavioral sciences will be extended to include multiple logistic regression, power and sample size considerations, and computer-intensive methods such as bootstrapping and randomization tests, which will considerably expand the repertoire of methods that a person could use. *Prerequisite:* STAT 5050 or equivalent.

**5070. Statistical Methods for the Social Sciences. 3.** General statistical analyses and their application to the social sciences. Analysis of variance, regression and correlation methods are studied from a data analytic perspective, emphasizing the conceptual understanding of where and when these techniques should be used and the interpretation of their results. Available computer programs will be utilized. Credit cannot be earned in more that one of the following courses: STAT 2110, 3050, 5050, 5060, 5070. Cross listed with SOC 5070. *Prerequisite:* one course in statistics (all introductory courses except STAT 2000).

**5080. Statistical Methods for the Agricultural and Natural Resource Sciences. 3.** Brief review of statistical principles. Use of SAS programming. Numerous analysis of variance techniques along with commonly-used experimental designs. Multiple mean comparison, linear contrasts, power of F test, simple linear regression, polynomial regression, analysis of covariance, and some categorical data techniques for students in the agriculture and natural resources sciences. Credit cannot be earned in more that one of the following courses: STAT 2110, 3050, 5050, 5060, 5070, 5080. Cross listed with ENTO 5080. *Prerequisite:* STAT 2050 or equivalent.

**5115. Time Series Analysis and Forecasting. 3.** An applied introduction to time series and forecasting. Brief coverage of time series regression, decomposition methods, and smoothing will lead into a more detailed coverage of Box-Jenkins (ARIMA) modeling. Computer analysis using MINITAB and SAS will be an important part of the course. Dual listed with STAT 4115; cross listed with ECON 5115. *Prerequisite:* STAT 3050 or equivalent; STAT 4015/5015 recommended.

**5155. Fundamentals of Sampling. 3.** Develops methodology of simple random sampling, stratified sampling, and multistage samples. Provides applications related to physical, social, and biological sciences. Discusses single and two-variable estimation techniques. Presents estimation based on subsamples from subpopulations. Dual listed with STAT 4155. *Prerequisite:* STAT 2070 or equivalent.

**5185. Analysis of Data. 3.** Focuses on data collection, analysis, interpretation, and communication, using contexts relevant to everyday situations. Topics chosen integrate well with the concerns of middle-level teachers and connect with such curriculum areas as health, science, and social studies. This course is not a research methods course. Cross listed with NASC 5180. *Prerequisites:* graduate standing in either degree or non-degree seeking status, and acceptance into the Middle-level mathematics program.

**5210. Advanced Regression. 3.** Advanced methodologies, with particular focus on concepts and methods related to regression. Topics include generalized linear models, nonlinear regression, elementary linear model theory, and Data Science topics such as resampling inference, ridge regression and the lass, and k-fold cross-validation. *Prerequisites:* MATH/STAT 4265/5265 and STAT 4015/5015. STAT 4025/5025 and STAT 4045/5045 are recommended.

**5220. Advanced Design. 3.** Advanced study of experimental designs, observational designs, and mixed models. Topics include fixed and random effects, factorial, split-plot and repeated measures designs, and hierarchical models. Linear model methodology and Data Science concepts will also be emphasized. *Prerequisites:* MATH/STAT 4265/5265, and at least one of STAT 4015/5015, STAT 4025/5025, or STAT 5210.

**5230. Statistic Methods III. 4.** Continuation of topics in Statistical Methods from 5220; aimed at preparing students for advanced topics courses in Statistics. *Prerequisites:* STAT 5220 and 5520.

**5240. Data Mining. 3.** An introduction to statistical learning and data mining using techniques that have proven useful in recognizing patterns and making predictions. These techniques include both parametric and nonparametric models. Tools for computing and evaluating these techniques will also be studied. Dual listed with STAT 4240. *Prerequisite:* STAT 5015.

**5255. Mathematical Theory of Probability. 3.** Calculus-based. Introduces mathematical properties of random variables. Includes discrete and continuous probability distributions, independence and conditional probability distributions, independence and conditional probability, mathematical expectation, multivariate distributions and properties of normal probability law. Dual listed with STAT 4255; cross listed with MATH 5255. *Prerequisite:* grade of C or better in MATH 2210 or 2355.

**5265. Introduction to the Theory of Statistics. 3.** Presents derivations of theoretical and sampling distributions. Introduces theory of estimation and hypothesis testing. Dual listed with STAT 4265; cross listed with MATH 5265. *Prerequisite:* STAT 4255/5255.

**5270. Applied Bayesian Statistics. 3.** This course introduces Bayesian data analysis in an applied context. We will learn about Bayesian statistics primarily in a regression model context, taken broadly. A conceptual understanding of popular Markov Chain Monte Carlo algorithms will be provided. Dual listed with STAT 4270. *Prerequisite:* STAT 3050. STAT 4015/5015 recommended.

**5280. Models for Hierarchical Data. 3.** Provides an introduction to the modeling and analysis of correlated/hierarchical data from exponential family member distributions (i.e. presence/absence, count data, Gaussian data). Emphasis is on applications. Aimed to build off of a first course in regression analysis. Dual listed with STAT 4280. *Prerequisite:* STAT 5015.

**5300. Applied Multivariate Analysis. 3.** The application of multivariate statistical methods in behavioral science research. Topics include: multivariate regression, canonical correlation, discriminate analysis, factor analysis and multivariate regression, canonical correlation, discriminate analysis, factor analysis and multidimensional scaling. A wide range of computer assistance is incorporated. Dual listed with STAT 4300. *Prerequisite:* STAT 5050, 5060, 5070, 5080.

**5350. Survey Construction and Analysis. 3.** Examines the issues surrounding the construction (item wording, test theory, and numerical scales), assessment (sampling and psychometrics), and analysis (tem analysis, qualitative data analysis, and factor analysis) of survey instruments. Roughly a third of the course is devoted to each of these areas. Dual listed with STAT 4350. *Prerequisite:* STAT 3050.

**5360. Spatial Statistics. 3.** Emphasis is on a generalized linear model approach to the modeling of continuous data, placing model building and the various kriging methods into a single conceptual framework. Dual listed with STAT 4360. *Prerequisite:* STAT 4015.

**5370. Survival Analysis. 3.** Introduction to the modeling of time to event data as it arises in epidemiological and medical research. Topics include parametric and non-parametric estimation for censored data without covariates, and for data with covariates, the proportional hazards regression model, additive hazards regression model and parametric regression models. Dual listed with STAT 4370. *Prerequisites:* STAT 4015, 4025 and 4265.

**5380. Bayesian Data Analysis. 3.** Bayesian statistical methods for analyzing various kinds of data. Topics include basic Bayesian ideas and model formulation (priors, posteriors, likelihoods), single- and multiple-parameter models, hierarchical models, generalized linear models, multivariate models, survival models and an introduction to computation methods. *Prerequisites:* at least 2 semesters of calculus and one semester of statistics at or beyond the 4000 level.

**5470. Data Analysis. 3.** This course is designed to develop the skill of analyzing data sets using methods of classic statistical analysis, such as analysis of variance, regression, discrete models, descriptive analysis, non-parametrics, and multivariate methods. The focus will be on understanding the various models and methods, computer assisted data analysis, and communication of results (oral and written). *Prerequisite:* 12 graduate level hours in statistics (excluding STAT 5000).

**5490. Statistical Consulting. 1.** An introduction to the art and practice of statistical consulting. Topics include active listening, ascertaining client knowledge level and ability, determining appropriate methods of analysis given limitations, and organizing and managing a consulting session. *Prerequisite:* graduate standing in statistics, 15 hours in statistics.

**5510. Distribution Theory. 4.** Topics covered include probability theory, conditional probability, random variables, special distribution functions, functions of random variables, expectation, random samples, and limiting distributions. *Prerequisite:* MATH 2210, 3000 or MATH/STAT 4265.

**5520. Inference I. 4.** Topics covered include Properties of a random sample, Sufficiency principle, Likelihood principle, point estimation (mle, mom, Bayes estimators, etc. and methods for evaluating estimators), some interval estimation. *Prerequisite:* STAT 5510.

**5530. Inference II. 3.** Topics covered include methods used in Bayesian, Likelihood, Frequentist inference; some methods for robust inference and some large sample theory as needed. *Prerequisite:* STAT 5520.

**5540. Large Sample Theory. 3.** Treats various limiting techniques which can be used to predict the behavior of statistics computed from large data sets. The characteristic function is used in deriving the law of large numbers and various forms of the central limit theorem, including the multivariate normal case. The central and noncentral chi-square distributions are derived as the probability law for certain statistics in the limit. Other topics discussed include modes of probabilistic convergence, speed of convergence, and large sample approximation procedures. *Prerequisite:* STAT 5510.

**5615. Time Series Analysis II. 3.** A treatment of theory and application of ARIMA modeling of times series. Frequency domain analysis is also introduced. Additional topics will be selected from intervention analysis, transfer function (ARMAX) models, outlier analysis, vector ARIMA models, ARCH, GARCH, and state-space models, according to the interests and abilities of the class. *Prerequisites:* STAT 4015/5015, 4115 and 4265/5265.

**5620. Theory of Linear Models. 3.** A theoretical approach to estimation and testing in the general linear model. Topics include: special linear algebra results for statistics, para-meterizations, estimability, least squares, best linear unbiased estimation, and testing linear hypotheses. *Prerequisite:* STAT 5630, 5520, MATH 4500.

**5630. Multivariate Analysis. 3.** The subject matter includes derivation of multi-variate normal distributions, the Wishart, and related sampling distributions, multivariate estimation, confidence regions, and hypothesis testing are covered including topics as Hotelling's T squared, profile analysis, discriminate analysis, factor analysis, and cluster analysis. *Prerequisite:* STAT 4265, MATH 2250.

**5650. Theory of Sampling. 3.** Consists of the theory of simple random sampling, stratified sampling, multistage sampling, and regression and ratio estimation. Recent developments in sampling are presented. *Prerequisite:* STAT 4265, STAT 4155/5155.

**5660. Computationally Intensive Methods in Statistics. 3.** Advanced statistical inference often relies on methods which are computationally intensive. The basic methods include Newton-Raphson; the EM algorithm; bootstrap and other resampling procedures; kernel density estimators; Laplace's method, importance sampling and MCMC, and saddlepoint and Edgeworth approximations. *Prerequisite:* STAT 5520.

**5670. Mixed Models. 3.** An advanced treatment of models with fixed and random effects. Topics include: model definitions, least- squares, analysis of variance techniques, likelihood procedures, and computational applications. *Prerequisite:* STAT 5620.

**5680. Advanced Bayesian Statistics. 3.** Philosophical principles underlying Bayesian and non-Bayesian statistics. Decision theoretic foundations of Bayesian statistics including loss functions, minimaxity, and admissibility. Construction of conjugate prior distributions and non-informative prior distributions. Bayesian point estimation, hypothesis tests and credible sets. Computational tools for Bayesian problems including Markov chain Monte Carlo (McMC) and other methods for approximating posterior distributions with some emphasis on implementation via a programming language or statistical computing software. As time and interest permit: the normal linear model, non-normal models, hierarchical models, Bayesian model averaging, other topics. *Prerequisites:* STAT 5380; 5420 and 5520.

**5810. Seminar. 1-2 (Max. 4).** Research results are presented by statistics majors. (Faculty also present papers). *Prerequisite:* graduate status in statistics.

**5820. Teaching of Statistics. 1 - 2. (Max 2).** The following topics are presented and discussed: traditional and innovative teaching methods, assessment methods, the purpose of lectures and laboratories, in-class activities, projects, mathematics versus statistics, computer assistance, math anxiety, and group and one-on-one interaction guidelines. *Prerequisite:* consent of instructor.

**5880. Advanced Problems. 1-8 (Max. 8).** Intended to develop the graduate student's ability to expand his theoretical knowledge by using library materials and working under close supervision of a faculty member who is an expert in the area of study. *Prerequisite:* 12 hours in statistics and consent of instructor.

**5890. Advanced Topics. 1-3 (Max. 3).** Special offerings beyond formal course work in thesis areas. *Prerequisite:* graduate status.

**5920. Continuing Registration: On Campus. 1-2 (Max. 16).** *Prerequisite:* advanced degree candidacy.

**5940. Continuing Registration: Off Campus. 1-2 (Max. 16).** *Prerequisite:* advanced degree candidacy.

**5959. Enrichment Studies. 1-3 (Max. 99).** Designed to provide an enrichment experience in a variety of topics. Note: credit in this course may not be included in a graduate program of study for degree purposes.

**5960. Thesis Research. 1-12 (Max. 24).** Graduate level course designed for students who are involved in research for their thesis project. Also used for students whose coursework is complete and are writing their thesis. *Prerequisite:* enrollment in a graduate degree program.

**5980. Dissertation Research. 1-12 (Max. 48).** Graduate level course designed for students who are involved in research for their dissertation project. Also used for students whose coursework is complete and are writing their dissertation. *Prerequisite:* enrollment in a graduate level degree program.

**5990. Internship. 1-12 (Max 24).** *Prerequisite:* graduate standing.