(Formerly MTH 291/ SDS 291). 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. Students who have completed SDS 100 in a previous semester need not repeat it. Corequisite: SDS 100.

(Formerly MTH 291/ SDS 291). 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. Students who have completed SDS 100 in a previous semester need not repeat it. Corequisite: SDS 100.

(Formerly MTH/SDS 290). 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. Special attention is given to analysis of data from student projects such as theses and special studies. Statistical software is used for data analysis.

This course is not about data analysis—rather, students learn the R programming language at a deep level. Topics may include data structures, control flow, regular expressions, functions, environments, functional programming, object-oriented programming, debugging, testing, version control, documentation, literate programming, code review and package development. The major goal for the course is to contribute to a viable, collaborative, open-source, publishable R package. Prerequisites: SDS 192 and CSC 110, or equivalent. Enrollment limited to 40.

(Formerly SDS 201). An application-oriented introduction to statistical modeling, covering topics of descriptive statistics, data visualization, point and interval estimates, bivariate and multiple regression modeling, and inferential hypothesis tests using both distributional and resampling methods. Lectures include “hands on” demonstrations of statistical phenomenon, with labs and assignments that emphasize analysis of real data. Students who have completed SDS 100 in a previous semester need not repeat it. Corequisite: SDS 100.

(Formerly SDS 201). An application-oriented introduction to statistical modeling, covering topics of descriptive statistics, data visualization, point and interval estimates, bivariate and multiple regression modeling, and inferential hypothesis tests using both distributional and resampling methods. Lectures include “hands on” demonstrations of statistical phenomenon, with labs and assignments that emphasize analysis of real data. Students who have completed SDS 100 in a previous semester need not repeat it. Corequisite: SDS 100.

(Formerly SDS 201). An application-oriented introduction to statistical modeling, covering topics of descriptive statistics, data visualization, point and interval estimates, bivariate and multiple regression modeling, and inferential hypothesis tests using both distributional and resampling methods. Lectures include “hands on” demonstrations of statistical phenomenon, with labs and assignments that emphasize analysis of real data. Students who have completed SDS 100 in a previous semester need not repeat it. Corequisite: SDS 100.