This course studies introductory statistics and probability, visual methods for summarizing quantitative information, basic experimental design and sampling methods, ethics and experimentation, causation, and interpretation of statistical analyzes. Applications use …
This course provides an introduction to the process of collecting, manipulating, exploring, analyzing, and displaying data using the statistical software R. The collection of elementary statistical analysis techniques introduced will …
This course includes a basic treatment of probability, and covers inference for one and two populations, including both hypothesis testing and confidence intervals. Analysis of variance and linear regression are …
This course provides an introduction to the probability & statistical theory underlying the estimation of parameters & testing of statistical hypotheses, including those in the context of simple & multiple …
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This course introduces methods to approach uncertainty and variation inherent in elementary statistical techniques from multiple angles. Simulation techniques such as the bootstrap will also be used. Conceptual discussion in …
This course provides an overview of basic probability and matrix algebra required for statistics. Topics include sample spaces and events, properties of probability, conditional probability, discrete and continuous random variables, …
This course provides a calculus-based introduction to mathematical statistics with some applications. Topics include: sampling theory, point estimation, interval estimation, testing hypotheses, linear regression, correlation, analysis of variance, and categorical …
This course provides a survey of regression analysis techniques, covering topics from simple regression, multiple regression, logistic regression, and analysis of variance. The primary focus is on model development and …
This course provides an introduction to data analysis using the Python programming language. Topics include using an integrated development environment; data analysis packages numpy, pandas and scipy; data loading, storage, …
This course introduces methods for presenting data graphically and in tabular form, including the use of software to create visualizations. Also introduced are databases, with topics including traditional relational databases …
This course includes an overview of parametric vs. non-parametric methods including one-sample, two-sample, and k-sample methods; pair comparison and block designs; tests for trends and association; multivariate tests; analysis of …
This course includes linear regression models, inferences in regression analysis, model validation, selection of independent variables, multicollinearity, influential observations, and other topics. Conceptual discussion is supplemented with hands-on practice in …
This course introduces various topics in experimental design, including simple comparative experiments, single factor analysis of variance, randomized blocks, Latin squares, factorial designs, blocking and confounding, and two-level factorial designs. …
This course focuses on applying data analytic techniques to business, including customer analytics, business analytics, and web analytics through mining of social media and other online data. Several projects are …
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Reading and study programs in areas of interest to individual students. For students interested in topics not covered in regular courses. Students must obtain a faculty advisor to approve and …
Students will work in teams on a capstone project. The project will involve significant data preparation and analysis of data, preparation of a comprehensive project report, and presentation of results. …
Introduces various topics in machine learning, including regression, classification, resampling methods, linear model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. The statistical software R is incorporated …
Research into current statistical problems under faculty supervision.