Statistics | STAT
P Prerequisite | C Co-requisite | R Recommended
I Fall Semester | II Spring Semester | S Summer Session/s
- STAT-I 414 Introduction to Design of Experiments (3 cr.) Online Collaborative Degree. The course offers comprehensive coverage of the key elements of experimental design used by applied researchers to solve problems in the field. It shows students how to use applied statistics for planning, running, and analyzing experiments. The emphasis is placed on the basic philosophy of design. The course requires the use of the software such as SAS, Minitab or R.
- STAT-I 421 Modern Statistical Modeling Using R and SAS (3 cr.) An introductory course on statistical computation. The primary goals of this course are (i) to introduce popular statistical software SAS and R and to develop basic data analysis skills, and (ii) to introduce basic statistical computation methods used in applications.
- STAT-S 352 Data Modeling and Inference (3 cr.) P: MATH-M 466 or consent of instructor. Intermediate-level survey of resampling, likelihood, and Bayesian methods of statistical inference. Distributional models of various data types. Categorical, count, time-to-event, time series, linear models, and hierarchical regression models.
- STAT-S 412 Statistical Learning Using R (3 cr.) P: MATH-M 301 and one of the following courses: MATH-M 466 or MATH-M 365 or MATH-M 261, with a grade of C- or better in each course; or consent of instructor. This course emphasizes the applications of statistical learning and data mining with the least of mathematical details using standard computer packages in R. Topics include the methods of supervised learning: regression, classification, resampling methods, tree-based methods, and support vector machines. Some unsupervised learning methods are also covered.
- STAT-S 437 Categorical Data Analysis (3 cr.) P: MATH-M 261, MATH-M 365, or MATH-M 466. The analysis of cross-classified categorical data. Loglinear models; regression models in which the response variable is binary, ordinal, nominal, or discrete. Logit, probit, multinomial logit models; logistic and Poisson regression.
- STAT-S 450 Time Series Analysis (3 cr.) P: MATH-M 466 and (MATH-M 574 or STAT-S 431), or consent of instructor Introduces techniques for analyzing data collected at different points in time. Emphasizes probability models, forecasting methods, analysis in both time and frequency domains, linear systems, state-space models, intervention analysis, transfer function models and the Kalman filter. Explores stationary processes, autocorrelations, and autoregressive, moving average, and ARMA processes, among other topics.
- STAT-S 470 Exploratory Data Analysis (3 cr.) P: STAT-S 352 or consent of instructor. Techniques for summarizing and displaying data. Exploration versus confirmation. Connections with conventional statistical analysis and data mining. Applications to large data sets.
- STAT-S 512 Statistical Learning and Data Analytics (3 cr.) P: MATH-M 301, MATH-M 466 or MATH-M 365 or MATH-M 261 or consent of instructor. This course emphasizes the fundamentals of statistical learning & data mining & their applications with the least of mathematical details. Topics include methods of supervised: regression, classification, resampling methods, tree-based methods, and support vector machines. Some unsupervised learning methods are also covered. The methods are illustrated with real data examples
- STAT-S 520 Introduction to Statistics (3 cr.) Online Collaborative Degree. P: Check schedule of classes. Basic concepts of data analysis and statistical inference, applied to 1-sample and 2-sample location problems, the analysis of variance, and linear regression. Probability models and statistical methods applied to practical situations and actual data sets from various disciplines. Elementary statistical theory, including the plug-in principle, maximum likelihood, and the method of least squares.