Same as SDS 220. An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. MTH 220 satisfies the basis requirement for biological science, engineering, environmental science, neuroscience and psychology.

Same as SDS 220. An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. MTH 220 satisfies the basis requirement for biological science, engineering, environmental science, neuroscience and psychology.

This one-semester course leverages students’ previous coursework to address a real-world data analysis problem. Students collaborate in teams on projects sponsored by academia, government, and/or industry. Professional skills developed include: ethics, project management, collaborative software development, documentation, and consulting. Regular team meetings, weekly progress reports, interim and final reports, and multiple presentations are required. Open only to majors. Prerequisites: SDS 192, SDS 291 and CSC 111.

Same as MTH 320. An introduction to the mathematical theory of statistics and to the application of that theory to the real world. Topics include functions of random variables, estimation, likelihood and Bayesian methods, hypothesis testing and linear models. Prerequisites: a course in introductory statistics, MTH 212 and MTH 246, or permission of the instructor.

In the era of “big data,” statistical models are becoming increasingly sophisticated. This course begins with linear regression models and introduces students to a variety of techniques for learning from data, as well as principled methods for assessing and comparing models. Topics include bias-variance trade-off, resampling and cross-validation, linear model selection and regularization, classification and regression trees, bagging, boosting, random forests, support vector machines, generalized additive models, principal component analysis, unsupervised learning and k-means clustering.

Same as MTH 291. Formerly MTH 247. Theory and applications of regression techniques; linear and nonlinear multiple regression models, residual and influence analysis, correlation, covariance analysis, indicator variables and time series analysis. This course includes methods for choosing, fitting, evaluating and comparing statistical models and analyzes data sets taken from the natural, physical and social sciences. Prerequisite: one of the following: MTH 201/PSY 201, GOV 190, MTH 219, MTH 220, ECO 220, or the equivalent or a score of 4 or 5 on the AP Statistics examination.

Same as MTH 291. Formerly MTH 247. Theory and applications of regression techniques; linear and nonlinear multiple regression models, residual and influence analysis, correlation, covariance analysis, indicator variables and time series analysis. This course includes methods for choosing, fitting, evaluating and comparing statistical models and analyzes data sets taken from the natural, physical and social sciences. Prerequisite: one of the following: MTH 201/PSY 201, GOV 190, MTH 219, MTH 220, ECO 220, or the equivalent or a score of 4 or 5 on the AP Statistics examination.

Same as MTH 290. Note: This course is no longer considered the same as PSY 301, starting in the 2014–15 academic year. A survey of statistical methods needed for scientific research, including planning data collection and data analyses that provide evidence about a research hypothesis. The course can include coverage of analyses of variance, interactions, contrasts, multiple comparisons, multiple regression, factor analysis, causal inference for observational and randomized studies and graphical methods for displaying data.

Same as MTH 220. (Formerly MTH 245). An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data.

Same as MTH 220. (Formerly MTH 245). An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data.