An introduction to probability and statistical modeling and its application to natural, physical and social science and related disciplines. Data analysis and interpretation, using computer software, are emphasized. Topics include random variables, probability distributions, expectation, estimation, testing, experimental design, quality control, resampling-based inference, and multiple regression. Limited to 25 students. Prerequisite: MTH 111, or MTH 153, or one year of high school calculus, or permission of the instructor.

An introduction to the concepts of abstract algebra, including groups, quotient groups, and, if time allows, rings, and fields. Prerequisites: MTH 153 and MTH 211, or permission of the instructor.

Functions of several variables, vector fields, divergence and curl, critical point theory, implicit functions, transformations and their Jacobians, theory and applications of multiple integration, and the theorems of Green, Gauss, and Stokes. Prerequisites: MTH 211 and MTH 212, or permission of the instructor.

Theory and applications of limits, derivatives, and integrals of functions of one, two and three variables. Curves in two and three dimensional space, vector functions, double and triple integrals, polar, cylindrical, spherical coordinates. Path integration and Green's Theorem. Prerequisites: MTH 112 or MTH 114. It is suggested that MTH 211 be taken before or concurrently with MTH 212.

Vector spaces, matrices, linear transformations, systems of linear equations. Applications to be selected from differential equations, foundations of physics, geometry, and other topics. Students may not receive credit for both MTH 211 and MTH 221. Prerequisite: MTH 112 or the equivalent, or MTH 111 and MTH 153; MTH 153 is suggested.

Vector spaces, matrices, linear transformations, systems of linear equations. Applications to be selected from differential equations, foundations of physics, geometry, and other topics. Students may not receive credit for both MTH 211 and MTH 221. Prerequisite: MTH 112 or the equivalent, or MTH 111 and MTH 153; MTH 153 is suggested.

Same as CSC 205. This course integrates the use of mathematics and computers for modeling various phenomena drawn from the natural and social sciences. Scientific topics, organized as case studies, will span a wide range of systems at all scales, with special emphasis on the life sciences. Mathematical tools include data analysis, discrete and continuous dynamical systems and discrete geometry. The course will provide training through programming in Mathematica and/or MATLAB. Prerequisites: MTH 112 or MTH 114. CSC 111 recommended. Enrollment limited to 20.

Same as PSY 190. 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 will be discussed. Applications are emphasized, and students use SPSS and other statistical software for data analysis. Classes meet for lecture/discussion and a required laboratory that emphasizes the analysis of real data.

Same as PSY 190. 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 will be discussed. Applications are emphasized, and students use SPSS and other statistical software for data analysis. Classes meet for lecture/discussion and a required laboratory that emphasizes the analysis of real data.

Same as PSY 190. 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 will be discussed. Applications are emphasized, and students use SPSS and other statistical software for data analysis. Classes meet for lecture/discussion and a required laboratory that emphasizes the analysis of real data.