This is a high-intermediate course that aims at increasing students’ ability to communicate comfortably in Spanish (orally and in writing). The course explores an array of issues relevant to the Spanish-speaking world, and prepares students to think more critically and in depth about those issues, with the goal of achieving a deeper understanding of the target cultures. Materials used in the class include visual narratives (film), short stories, poems, plays and essays. Enrollment limited to 25. Prerequisite: SPN 200 or the equivalent.

(Formerly MTH/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. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology.

(Formerly MTH/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. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology.

(Formerly MTH/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. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology.

(Formerly MTH/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. Prerequisite: one of the following: SDS 201, PSY 201, GOV 190, SDS 220, ECO 220, or the equivalent or a score of 4 or 5 on the AP Statistics examination. Enrollment limited to 30.

Topics in statistics and data science. Statistical methods for analyzing data must be chosen appropriately based on the type and structure of the data being analyzed. The particular methods and types of data studied this in this course vary, but topics may include: categorical data analysis, time series analysis, survival analysis, structural equation modeling, survey methodology, Bayesian methods, resampling methods, spatial statistics, missing data methods, advanced linear models, statistical/machine learning, network science, relational databases, web scraping and text mining.

Topics in statistics and data science. Statistical methods for analyzing data must be chosen appropriately based on the type and structure of the data being analyzed. The particular methods and types of data studied this in this course vary, but topics may include: categorical data analysis, time series analysis, survival analysis, structural equation modeling, survey methodology, Bayesian methods, resampling methods, spatial statistics, missing data methods, advanced linear models, statistical/machine learning, network science, relational databases, web scraping and text mining.

(Formerly MTH/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. Prerequisite: one of the following: SDS 201, PSY 201, GOV 190, SDS 220, ECO 220, or the equivalent or a score of 4 or 5 on the AP Statistics examination. Enrollment limited to 30.

(Formerly MTH/PSY 201). An overview of the statistical methods needed for undergraduate research emphasizing methods for data collection, data description and statistical inference including an introduction to study design, confidence intervals, testing hypotheses, analysis of variance and regression analysis. Techniques for analyzing both quantitative and categorical data are discussed. Applications are emphasized, and students use R for data analysis. Classes meet for lecture/discussion and a required laboratory that emphasizes the analysis of real data.

(Formerly MTH/PSY 201). An overview of the statistical methods needed for undergraduate research emphasizing methods for data collection, data description and statistical inference including an introduction to study design, confidence intervals, testing hypotheses, analysis of variance and regression analysis. Techniques for analyzing both quantitative and categorical data are discussed. Applications are emphasized, and students use R for data analysis. Classes meet for lecture/discussion and a required laboratory that emphasizes the analysis of real data.