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Undergraduate Courses

Introductory statistics, including the collection of data, descriptive statistics, probability, and inference. Topics include sampling methods, experiments, numerical and graphical descriptive methods, correlation and regression, contingency tables, probability concepts and distributions,…

Introductory statistics, including the collection of data, descriptive statistics, probability, and inference. Topics include sampling methods, experiments, numerical and graphical descriptive methods, correlation and regression, contingency tables, probability concepts and distributions,…

In-depth introductory statistical methods, focusing on inference, alignment between study design and conclusions, and real-world decision making. Includes parametric and non-parametric approaches to one- and two-sample inference for means and proportions, Type I and II errors, power; chi-squared…

Sampling theory including sample survey design; descriptive statistics; statistical distributions and their uses; estimation; introductory statistical inference including z-test and t-test for one sample (hypothesis testing); analysis of differences in two means; simple linear regression and…

Elementary statistical analysis and data manipulation in R. Topics include algorithms, programs, and computing in R. Fundamental techniques of program development in R. Programming projects and applications. Hands-on experience of data input/output and formatting, brief introduction to object-…

Elementary statistical analysis and data manipulation in R. Topics include algorithms, programs, and computing in R. Fundamental techniques of program development in R. Programming projects and applications. Hands-on experience of data input/output and formatting, brief introduction to object-…

Applied approach to statistical investigation, focusing on real- world decision making in the face of uncertainty. Introduction to central limit theorem and sampling distributions from probabilistic and simulations frameworks for inference, including one- and two-sample inference for means,…

Applied approach to statistical investigation, focusing on real- world decision making in the face of uncertainty. Introduction to central limit theorem and sampling distributions from probabilistic and simulations frameworks for inference, including one- and two-sample inference for means,…

An understanding of probability and uncertainty in real-world situations. Case studies of the role of uncertainty in the life sciences. Analyzing chance phenomena to identify the underlying probabilistic principles and translating them into probability distributions or simulations. Introduction…

Univariate analysis for measurement data using graphs and numerical summaries; bivariate analysis for measurement data using scatterplots, correlation, and fitting lines; describing categorical data; sampling methods; observational and experimental studies; describing random behavior; binomial…

Univariate analysis for measurement data using graphs and numerical summaries; bivariate analysis for measurement data using scatterplots, correlation, and fitting lines; describing categorical data; sampling methods; observational and experimental studies; describing random behavior; binomial…

Sampling and statistical studies; basic probability; random variables and their distributions; exploring data using graphical techniques and numerical summaries; exploring relationships between two variables: chi-sq. test of independence; correlation, linear regression; confidence intervals and…

Stochastic processes including discrete, continuous and conditional probability concepts. Definitions and properties of stochastic processes. Markov processes and chains, basic properties, transition matrices and steady state properties. Reliability renewal and queueing processes, expected…

Analysis of variance including completely randomized design, randomized block design, factorial designs, and interaction; regression analysis including linear regression and multiple regression, model checking and analysis of residuals, and model building; nonparametric statistics; power of a…

A survey of statistical methods that introduces experimental design and analysis of variance; multiple linear regression; analysis of categorical data, including chi-squared tests of independence and goodness-of-fit; non-parametric tests, including tests based on resampling; and statistical…

Constructing and analyzing statistical experimental designs; blocking, randomization, replication, and interaction; complete and incomplete block designs; factorial experiments; repeated measures; confounding effects; orthogonal arrays; computer experiments and simulations; design and analysis…

Applied methods in regression analysis with implementation in R. Topics include linear regression with mathematical examination of model assumptions and inferential procedures; multiple regression and model building, including collinearity, variable selection and inferential procedures; ANOVA as…

Design of finite population sample surveys. Stratified, systematic, and multistage cluster sampling designs. Sampling with probability proportional to size. Auxiliary variables, ratio and regression estimators, non-response bias.

 

The methodology of multivariate statistics and machine learning for students specializing in statistics. Topics include inference on multivariate means, multivariate analysis of variance, principal component analysis, linear discriminant analysis, factor analysis, linear discrimination,…

Basic graphical techniques and control charts. Experimentation in quality assurance. Sampling issues. Other topics include process capability studies, error analysis, SPRT, estimation and reliability.

Offered spring semester every even-numbered year. 

Autoregressive, moving average, autoregressive-moving average, and integrated autoregressive-moving average processes, seasonal models, autocorrelation function, estimation, model checking, forecasting, spectrum, spectral estimators.

Techniques and applications of nonparametric statistical methods, estimates, confidence intervals, one sample tests, two sample tests, several sample tests, tests of fit, nonparametric analysis of variance, correlation tests, chi-square test of independence and homogeneity, sample size…

Introduction to theory and methods of the Bayesian approach to statistical inference and data analysis. Covers components of Bayesian analysis (prior, likelihood, posterior), computational algorithms, and philosophical differences among various schools of statistical thought.

Offered…

A second course in statistical computing, using the SAS programming language to read data, create and manipulate SAS data sets, writing and using SAS MACROS, and SAS programming efficiency. SAS-based implementation of Structured Query Language (SQL). Additional topics may include Hadoop and…

Programming techniques in modern statistical software, including SAS and R for students with some experience with computer programming. Topics include data input/output; data formats and types; data management; flow control, conditional execution, and program design; statistical graphics and…

Programming techniques in modern statistical software, including SAS and R for students with some experience with computer programming. Topics include data input/output; data formats and types; data management; flow control, conditional execution, and program design; statistical graphics and…

Statistical analysis and data manipulation in R and Python. Implementation of SQL. Topics include data input/output; data formats and types; data management; functions for statistical modeling; introduction to algorithms; flow control and program design; and programs for complex data…

Methods for comparing time-to-event data, including univariate parametric and nonparametric procedures, regression models, diagnostics, group comparisons, and use of relevant statistical computing packages.

Methods for comparing time-to-event data, including univariate parametric and nonparametric procedures, regression models, diagnostics, group comparisons, and use of relevant statistical computing packages.

Concepts and basic properties of some special probability distributions, independence, moment generating functions, sampling distributions of statistics, limiting distributions.

Introduction to the fundamentals of statistical inference. Point estimation, including the properties of estimators and ways of evaluating or comparing them, confidence intervals, and hypothesis testing. Statistical inference in linear models, including regression and analysis of variance, is…

Mathematical and computational approaches to estimation and inference from frequentist and Bayesian perspectives. Sampling distributions; maximum likelihood estimation; computational maximization of likelihoods, including grid search, Newton- Raphson methods; likelihood ratio tests. Simulations…

The methodology of categorical data analysis and its applications. The course covers descriptive and inferential methods for contingency tables, an introduction to generalized linear models, logistic regression, multinomial response models, regression for counts, and methods for categorical data…

Applications of mathematics and statistics in biology. Topics such as gene mapping, inference of evolutionary relationships, epidemiological modeling, and brain imaging. Students will conduct mini-projects on each topic. Emphasis on (1) thought process of converting biological problems into…

Probability axioms, combinatorial analysis, random variables, univariate and multivariate distributions, expectations, conditional distributions, independence, and laws of large numbers.

Not offered on a regular basis.

Central limit theorems, random walks, Markov chains and processes, Brownian motion, branching and renewal processes, diffusion processes and queueing processes and applications.

Not offered on a regular basis.

This course will allow students the opportunity for independent study of one or more topics in statistics under the direction of the instructor.

Students' workload and time commitment to the course per credit hour will be commensurate with the per credit hour expectations in traditional…

Faculty-supervised independent or collaborative inquiry into fundamental and applied problems within a discipline that requires students to gather, analyze, synthesize, and interpret data and to present results in writing and other relevant communication formats.

This course belongs to a…

Faculty-supervised independent or collaborative inquiry into fundamental and applied problems within a discipline that requires students to gather, analyze, synthesize, and interpret data and to present results in writing and other relevant communication formats.

These courses belong to a…

Faculty-supervised independent or collaborative inquiry into fundamental and applied problems within a discipline that requires students to gather, analyze, synthesize, and interpret data and to present results in writing and other relevant communication formats.

These courses belong to a…

Provides an exposure to advanced methods and technologies in data science, including data acquisition, data quality, big data management and analytics, data mining, data security and privacy, and introduces the students to data science experience with a real-world problem. In addition, effective…

Provides an exposure to advanced methods and technologies in data science, including data acquisition, data quality, big data management and analytics, data mining, data security and privacy, and introduces the students to data science experience with a real-world problem. In addition, effective…

Faculty-supervised independent or collaborative inquiry into fundamental and applied problems within a discipline that requires students to gather, analyze, synthesize, and interpret data. Students will write or produce a thesis or other professional capstone product, such as a report or…

Provides undergraduate statistics majors with an exposure to advanced statistical methods, beyond regression and analysis of variance, and introduces the student to a data-analysis experience related to a real scientific problem. In addition to learning and applying statistical techniques,…

Provides an exposure to advanced statistical methods, beyond regression and analysis of variance, and introduces the student to a data-analysis experience related to a real scientific problem. In addition to learning and applying statistical techniques, effective oral and written communication…

Provides students with an exposure to advanced statistical methods, beyond regression and analysis of variance, and introduces the student to a data-analysis experience related to a real scientific problem. In addition to learning and applying statistical techniques, effective oral and written…

Provides students with an exposure to advanced statistical methods, beyond regression and analysis of variance, and introduces the student to a data-analysis experience related to a real scientific problem. In addition to learning and applying statistical techniques, effective oral and written…

Provides an exposure to advanced statistical methods, beyond regression and analysis of variance, and introduces the student to a data-analysis experience related to a real scientific problem. In addition to learning and applying statistical techniques, effective oral and written communication…

Supervised practicum in a government agency or industry site.

Students work within a statistics group under the direction of the group leader at the government or industry site. A report of the statistical consulting activity undertaken is submitted to the directing faculty member.

Supervised practicum in a government agency or industry site.

Students work within a statistics group under the direction of the group leader at the government or industry site. A report of the statistical consulting activity undertaken is submitted to the directing faculty member.

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