### STATISTICS

**STAT-099**

**Course Level:** Graduate

**STAT-196**

**Selected Topics: Non-recurring (1-6)**

**Course Level:** Undergraduate

Selected Topics: Non-Recurring (1-6) Topics vary by section. Repeatable for credit with different topic.

**STAT-202**

**Basic Statistics (4)**

**Course Level:** Undergraduate

Basic Statistics (4) Data presentation, display, and summary, averages, dispersion, simple linear regression, and correlation, probability, sampling distributions, confidence intervals, and tests of significance. Use of statistical software both to analyze real data and to demonstrate and explore concepts. Usually Offered: fall, spring, and summer. Prerequisite: mathematics course. Restriction: Registration not allowed in both STAT-202, and STAT-203 or STAT-204. Note: Students may not receive credit toward a degree for both STAT-202, and STAT-203 or STAT-204.

**STAT-203**

**Basic Statistics with Calculus (4)**

**Course Level:** Undergraduate

Basic Statistics with Calculus (4) A calculus-based introduction to basic statistics including data presentation, display and summary, correlation, development of least squares regression models, probability, independence, probability density functions, moments, use of moment generating functions, sampling distributions, confidence intervals, and tests of significance. Concepts are explored through simulation and the use of the calculus tools of finding maxima and minima of a function and the area under a curve. Usually Offered: fall and spring. Prerequisite: MATH-221. Restriction: Registration not allowed in both STAT-203, and STAT-202 or STAT-204. Note: Students may not receive credit toward a degree for both STAT-203, and STAT-202 or STAT-204.

**STAT-204**

**Introduction to Business Statistics (4)**

**Course Level:** Undergraduate

Introduction to Business Statistics (4) Statistical concepts and methods used in business decision making. Topics include probability rules, discrete and continuous distributions, descriptive and graphical statistics, estimation, confidence intervals, hypothesis testing, analysis of variance, regression, and applications of MS Excel data analysis tools to economic and business data. Usually Offered: fall and spring. Grading: A-F only. Prerequisite: MATH-150, MATH-151, MATH-154, or MATH-155. Restriction: Registration not allowed in both STAT-204, and STAT-202 or STAT-203. Note: Students may not receive credit toward a degree for both STAT-204, and STAT-202 or STAT-203.

**STAT-294**

**Community Service-Learning Project (1)**

**Course Level:** Undergraduate

Community Service-Learning Project (1) Grading: Pass/Fail only. Permission: instructor and Center for Community Engagement & Service.

**STAT-296**

**Selected Topics: Non-recurring (1-6)**

**Course Level:** Undergraduate

Selected Topics: Non-Recurring (1-6) Topics vary by section. Repeatable for credit with different topic.

**STAT-302**

**Intermediate Statistics (3)**

**Course Level:** Undergraduate

Intermediate Statistics (3) Methods include techniques for estimation and inference with qualitative and quantitative data focusing on simple linear and multiple regression, correlation, logistic regression, and analysis of variance. Usually Offered: fall and spring. Prerequisite: STAT-202 or STAT-203 with a grade of C or higher. Restriction: Registration not allowed in both STAT-302, and STAT-320 or STAT-514. Note: Students may not receive credit toward a degree for both STAT-302, and STAT-320, or STAT-514.

**STAT-320**

**Biostatistics (3)**

**Course Level:** Undergraduate

Biostatistics (3) This course examines how statistical methods are utilized within the fields of biology, medicine, and public health. Advanced statistical methods, including ANOVA, multiple regression, analysis of covariance, survival analysis, and nonparametric methods are discussed, with emphasis on their applicability to public health. Usually Offered: spring. Prerequisite: STAT-202 or STAT-203. Restriction: Registration not allowed in both STAT-320, and STAT-302 or STAT-514. Note: Students may not receive credit toward a degree for both STAT-320, and STAT-302, or STAT-514.

**STAT-390**

**Independent Reading Course in Statistics (1-6)**

**Course Level:** Undergraduate

Independent Reading Course in Statistics (1-6) Permission: instructor and department chair.

**STAT-394**

**Community Service-Learning Project (1)**

**Course Level:** Undergraduate

Community Service-Learning Project (1) Grading: Pass/Fail only. Permission: instructor and Center for Community Engagement & Service.

**STAT-396**

**Selected Topics: Non-recurring (1-6)**

**Course Level:** Undergraduate

Selected Topics: Non-Recurring (1-6) Topics vary by section. Repeatable for credit with different topic.

**STAT-402**

**Introduction to Mathematical Statistics (3)**

**Course Level:** Undergraduate

Introduction to Mathematical Statistics (3) Probability, probability distributions, sampling, sampling distributions, and introduction to the theory of point estimation and statistical inference, including confidence intervals and hypothesis testing. Usually Offered: spring. Prerequisite: MATH-401.

**STAT-405**

**Introduction to Survey Sampling (3)**

**Course Level:** Undergraduate

Introduction to Survey Sampling (3) This course introduces the basic approaches to surveys, including simple random, systematic, and stratified sampling. Also included is the design of questionnaires and the analysis of sample data. Emphasis is on the practical application of sampling. Crosslist: STAT-605. Usually Offered: alternate falls. Prerequisite: STAT-202, STAT-203, or STAT-514.

**STAT-412**

**Statistical Programming in R (3)**

**Course Level:** Undergraduate

Statistical Programming in R (3) The basics of programming using the open source statistical program R. Includes imputing data, performing basic analyses, graphing, data types, control structures and functions in base R, and using packages to expand R's capabilities. Crosslist: STAT-612. Usually Offered: fall. Prerequisite: two mathematics or statistic courses at the 200-level or above, or STAT-514.

**STAT-413**

**Data Science (3)**

**Course Level:** Undergraduate

Data Science (3) This course focuses on the collection, organization, analysis, interpretation, and presentation of data. Topics include the acquisition, cleaning, and imputation of data from a variety of sources; data visualization and graphing; data presentation and packaging; and programming considerations for large datasets. The course uses R packages and programming language. Crosslist: STAT-613. Usually Offered: spring. Prerequisite: STAT-412.

**STAT-415**

**Regression (3)**

**Course Level:** Undergraduate

Regression (3) Simple and multiple regression, least squares, curve fitting, graphic techniques, and tests and confidence intervals for regression coefficients. Crosslist: STAT-615. Usually Offered: fall and summer. Prerequisite: STAT-302, STAT-320, or STAT-514.

**STAT-422**

**Advanced Biostatistics (3)**

**Course Level:** Undergraduate

Advanced Biostatistics (3) Introduction to methodologies used to understand complex problems in four major areas of biomedical science: clinical trials, epidemiology, survival analysis, and bioinformatics. Phases of clinical trials, dynamics of epidemiology and disease, estimation of survival, hazard and mortality functions, and analysis of gene expression data are covered. Crosslist: STAT-622. Usually Offered: fall. Grading: A-F only. Prerequisite: STAT-302, or STAT-320, or STAT-514.

**STAT-425**

**Statistical Software (3)**

**Course Level:** Undergraduate

Statistical Software (3) Introduction to the use of the SAS language to prepare, modify, and analyze data, interpret output and final preparation of results. Emphasis on practical programming principles and use of built-in procedures. Comparisons with other programming languages. Crosslist: STAT-625. Usually Offered: fall. Prerequisite: STAT-514 or two statistics courses.

**STAT-427**

**Statistical Machine Learning (3)**

**Course Level:** Undergraduate

Statistical Machine Learning (3) Introduction to statistical concepts, models, and algorithms of machine learning. Explores supervised learning for regression and classification, unsupervised learning for clustering and principal components analysis, and related topics such as discriminant analysis, splines, lasso and other shrinkage methods, bootstrap, regression, and classification trees, and support vector machines, along with their tuning, diagnostics, and performance evaluation. Crosslist: STAT-627. Prerequisite: STAT-415.

**STAT-490**

**Independent Study Project in Statistics (1-6)**

**Course Level:** Undergraduate

Independent Study Project in Statistics (1-6) Permission: instructor and department chair.

**STAT-491**

**Internship in Statistics (1-6)**

**Course Level:** Undergraduate

Internship (1-6) Permission: instructor and department chair.

**STAT-496**

**Selected Topics: Non-recurring (1-6)**

**Course Level:** Undergraduate

**STAT-514**

**Statistical Methods (3)**

**Course Level:** Undergraduate/Graduate

Statistical Methods (3) Averages, dispersion, probability, sampling, and approach to normality; simple and multiple regression; tests and confidence intervals for means, proportions, differences, and regression coefficients; nonparametric statistics; and analysis of variance. Usually Offered: fall, spring, and summer. Prerequisite: STAT-202 or STAT-203. Restriction: Registration not allowed in both STAT-514, and STAT-302 or STAT-320. No credit toward mathematics or statistics graduate program. Note: Students may not receive credit toward a degree for both STAT-514, and STAT-302, or STAT-320.

**STAT-516**

**Design of Experiments (3)**

**Course Level:** Undergraduate/Graduate

Design of Experiments (3) Design and analysis of the results of balanced experiments, simple analysis of variance, components of variance, analysis of covariance, and related subjects. Usually Offered: spring. Prerequisite: STAT-302, STAT-320, or STAT-514.

**STAT-517**

**Special Topics in Statistical Methodology (3)**

**Course Level:** Undergraduate/Graduate

Special Topics in Statistical Methodology (3) Topics vary by section. Alternating topics in statistics from an applied viewpoint. Topics include sampling, multivariate techniques, factor analysis, and time series. Usually Offered: alternate summers (odd years). Repeatable for credit with different topic. Prerequisite: STAT-302, STAT-320, or STAT-514.

**STAT-519**

**Nonparametric Statistics (3)**

**Course Level:** Undergraduate/Graduate

Nonparametric Statistics (3) Application of nonparametric techniques in the analysis of social science data, with emphasis on tests appropriate for data having interval, nominal, and ordinal scales. Usually Offered: alternate falls (even years). Prerequisite: STAT-302, STAT-320, or STAT-514.

**STAT-520**

**Applied Multivariate Analysis (3)**

**Course Level:** Undergraduate/Graduate

Applied Multivariate Analysis (3) Introduction to multivariate analysis emphasizing statistical applications. Includes matrix theory, multivariate distributions, tests of hypotheses, multivariate analysis of variance, principal components, discriminant analysis, canonical correlation, multivariate regression, and related subjects. Usually Offered: alternate falls (odd years). Prerequisite: STAT-302, STAT-320, or STAT-514.

**STAT-521**

**Analysis of Categorical Data (3)**

**Course Level:** Undergraduate/Graduate

Analysis of Categorical Data (3) Chi-square tests, contingency tables (2X2, rXc, and multidimensional), loglinear models, and other special models. Usually Offered: alternate springs (even years). Prerequisite: STAT-302 or STAT-514.

**STAT-522**

**Time-Series Analysis (3)**

**Course Level:** Undergraduate/Graduate

Time-Series Analysis (3) An introduction to the theory of time-dependent data. The analysis includes modeling, estimation, and testing of data in the time domain using autoregressive and moving average models. Usually Offered: alternate springs (odd years). Prerequisite: STAT-415 or STAT-520 or STAT-615.

**STAT-584**

**Introduction to Stochastic Processes (3)**

**Course Level:** Undergraduate/Graduate

Introduction to Stochastic Processes (3) Introduction to random walks, Markov chains and processes, Poisson processes, recurrent events, birth and death processes, and related subjects. Usually Offered: alternate springs. Prerequisite: STAT-630.

**STAT-590**

**Independent Reading Course in Statistics (1-6)**

**Course Level:** Undergraduate/Graduate

Independent Reading Course in Statistics (1-6) Permission: instructor and department chair.

**STAT-596**

**Selected Topics: Non-recurring (1-6)**

**Course Level:** Undergraduate/Graduate

**STAT-601**

**Topics in Advanced Probability and Statistics (3)**

**Course Level:** Graduate

Topics in Advanced Probability and Statistics (3) Topics vary by section. Mathematical foundations of statistical theory. Special topics in probability and mathematical statistics. Usually Offered: alternate springs (odd years). Repeatable for credit with different topic. Permission: instructor.

**STAT-605**

**Introduction to Survey Sampling (3)**

**Course Level:** Graduate

Introduction to Survey Sampling (3) This course introduces the basic approaches to surveys, including simple random, systematic, and stratified sampling. Also included is the design of questionnaires and the analysis of sample data. Emphasis is on the practical application of sampling. Crosslist: STAT-405. Usually Offered: alternate falls. Prerequisite: STAT-514.

**STAT-610**

**Statistical Inference: Estimation (3)**

**Course Level:** Graduate

Statistical Inference: Estimation (3) The mathematical foundations of statistical inference; the Theory of Estimation including minimum risk-, Bayes-, minimax-, and equivariant estimation; decision theory; and large sample behavior. Usually Offered: alternate falls (even years). Prerequisite: STAT-632.

**STAT-611**

**Theory of Sampling (3)**

**Course Level:** Graduate

Theory of Sampling (3) This course covers the mathematical development of the principles of survey design, including methods for determining expected value, bias, variance, and mean square error; simple random, systematic, stratified, cluster, multistage, and double sampling; unbiased, ration, regression and composite estimation; measurement error; and comparison of alternative designs. Usually Offered: alternate springs. Prerequisite: STAT-605.

**STAT-612**

**Statistical Programming in R (3)**

**Course Level:** Graduate

Statistical Programming in R (3) The basics of programming using the open source statistical program R. Includes imputing data, performing basic analyses, graphing, data types, control structures and functions in base R, and using packages to expand R's capabilities. Crosslist: STAT-412. Usually Offered: fall. Grading: A-F only. Prerequisite: STAT-514.

**STAT-613**

**Data Science (3)**

**Course Level:** Graduate

Data Science (3) This course focuses on the collection, organization, analysis, interpretation, and presentation of data. Topics include the acquisition, cleaning, and imputation of data from a variety of sources; data visualization and graphing; data presentation and packaging; and programming considerations for large datasets. The course uses R packages and programming language. Crosslist: STAT-413 Usually Offered: spring. Grading: A-F only. Prerequisite: STAT-612.

**STAT-615**

**Regression (3)**

**Course Level:** Graduate

Regression (3) Simple and multiple regression, least squares, curve fitting, graphic techniques, and tests and confidence intervals for regression coefficients. Crosslist: STAT-415. Usually Offered: fall and summer. Prerequisite: STAT-514.

**STAT-616**

**Generalized Linear Models (3)**

**Course Level:** Graduate

Generalized Linear Models (3) Extension of regression methodology to more general settings where standard assumptions for ordinary least squares are violated. Generalized least squares, robust regression, bootstrap, regression in the presence of auto-correlated errors, generalized linear models, logistic and Poisson regression. Usually Offered: spring. Prerequisite: STAT-615 and a course in calculus.

**STAT-618**

**Bayesian Statistics (3)**

**Course Level:** Graduate

Bayesian Statistics (3) Principles and applications of modern statistical decision theory, with a special focus on Bayesian modeling, data analysis, inference, and optimal decision making. Prior and posterior; comparison of Bayesian and frequentist approaches, including minimax decision making and elementary game theory. Bayesian estimation, hypothesis testing, credible sets, and Bayesian prediction. Introduction to Bayesian computing software and applications to diverse fields. Grading: A-F only. Prerequisite: STAT-514.

**STAT-622**

**Advanced Biostatistics (3)**

**Course Level:** Graduate

Advanced Biostatistics (3) Introduction to methodologies used to understand complex problems in four major areas of biomedical science: clinical trials, epidemiology, survival analysis, and bioinformatics. Phases of clinical trials, dynamics of epidemiology and disease, estimation of survival, hazard and mortality functions, and analysis of gene expression data are covered. Crosslist: STAT-422. Usually Offered: fall. Grading: A-F only. Prerequisite: STAT-514.

**STAT-625**

**Statistical Software (3)**

**Course Level:** Graduate

Statistical Software (3) Introduction to the use of the SAS language to prepare, modify, and analyze data, interpret output and final preparation of results. Emphasis on practical programming principles and use of built-in procedures. Comparisons with other programming languages. Crosslist: STAT-425. Usually Offered: fall. Prerequisite: STAT-514 or two statistics courses.

**STAT-627**

**Statistical Machine Learning (3)**

**Course Level:** Graduate

Statistical Machine Learning (3) Introduction to statistical concepts, models, and algorithms of machine learning. Explores supervised learning for regression and classification, unsupervised learning for clustering and principal components analysis, and related topics such as discriminant analysis, splines, lasso and other shrinkage methods, bootstrap, regression, and classification trees, and support vector machines, along with their tuning, diagnostics, and performance evaluation. Crosslist: STAT-427. Grading: A-F only. Prerequisite: STAT-520 or STAT-615.

**STAT-630**

**Mathematical Statistics I (3)**

**Course Level:** Graduate

Mathematical Statistics I (3) Probability, random variables, probability distributions and functions of random variables, generating functions, order statistics, the theory of point estimation, (maximum likelihood, minimum variance unbiased estimators, confidence intervals), and theory of hypothesis testing (Neyman-Pearson, likelihood ratio, etc.). Usually Offered: fall.

**STAT-631**

**Mathematical Statistics II (3)**

**Course Level:** Graduate

Mathematical Statistics II (3) Probability, random variables, probability distributions and functions of random variables, generating functions, order statistics, the theory of point estimation, (maximum likelihood, minimum variance unbiased estimators, confidence intervals), and theory of hypothesis testing (Neyman-Pearson, likelihood ratio, etc.). Usually Offered: spring. Prerequisite: STAT-630.

**STAT-632**

**Advanced Mathematical Statistics (3)**

**Course Level:** Graduate

Advanced Mathematical Statistics (3) Theory of estimation, properties of estimators, large-sample properties and techniques, and applications. Usually Offered: fall. Prerequisite: STAT-631.

**STAT-690**

**Independent Study Project in Statistics (1-6)**

**Course Level:** Graduate

Independent Study Project in Statistics (1-6) Permission: instructor and department chair.

**STAT-691**

**Internship in Statistics (1-6)**

**Course Level:** Graduate

Internship in Statistics (1-6) Individual placement and supervision in an approved organization involving statistical analysis, methodology, or theory. Permission: instructor and department chair.

**STAT-696**

**Selected Topics: Non-recurring (1-6)**

**Course Level:** Graduate

**STAT-796**

**Selected Topics: Non-Recurring (1-6)**

**Course Level:** Graduate

**STAT-797**

**Master's Thesis Research (1-6)**

**Course Level:** Graduate

Master's Thesis Research (1-6) Grading: SP/UP only.

**STAT-798**

**Statistical Research and Consulting (1-3)**

**Course Level:** Graduate

Statistical Research and Consulting (1-3) Through written reviews and oral presentations, students investigate advances in statistical theory and applications in recent journals. Through interaction with other departments, students learn to formulate statistically problems expressed in the language of another discipline and interact in a consulting role with researchers outside of statistics. This course develops statistical consulting skills ranging from basic concepts relating to how to formulate the nature of the client's problem, to solving it and presenting the solution in the language of the client. Students complete major statistical consulting problems under supervision and statistical support for projects on campus as well as outside consulting projects. Usually Offered: spring. Repeatable for credit. Permission: department.