Topics course Curves, surfaces, and higher dimensional geometric configurations defined by polynomial equations. Relevant commutative algebra will be developed; consideration will be given to the role of algorithm in solving systems of polynomial equations. Prerequisites: A first course in abstract algebra such as Math 233.

Complex numbers, functions of a complex variable, algebra and geometry of the complex plane. Differentiation, integration, Cauchy integral formula, calculus of residues, applications. Prerequisite: MTH 225 or MTH 243, or permission of the instructor.

Topic: Research in Mathematics. The course is specifically designed for students in the Center for Women in Mathematics, but open to all serious mathematics students. Prerequisites: At least one of MTH 233, 238, or 243 and permission of the instructor. In this course students will work in small groups on original research projects. Prerequisites: At least one of MTH 233, 238, or 243 and permission of the instructor.

In the class we don't do math as much as we talk about doing math and the culture of mathematics. The class will include lectures by students, faculty and visitors on a wide variety of topics, and opportunities to talk with mathematicians about their lives. This course is especially helpful for those considering graduate school in the mathematical sciences. Prerequisites: MTH 211, MTH 212, and two additional mathematics courses at the 200 level, or permission of the instructor. May be repeated once for credit. This course is graded satisfactory/unsatisfactory only.

A survey of statistical methods needed for scientific research, including planning data collection and data analyses that will 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 will be used for data analysis.

Theory and applications of regression techniques; linear and non linear 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 190/PSY 190, GOV 190, MTH 241, MTH 245, ECO 220, or the equivalent or a score of 4 or 5 on the AP Statistics examination. Enrollment limited to 25.

An introduction to probability, including combinatorial probability, random variables, discrete and continuous distributions. Prerequisites: MTH 153 and MTH 212 (may be taken concurrently), or permission of the instructor.

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 will be used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. MTH 245 satisfies the basis requirement for Biological Science, Engineering, Environmental Science, Neuroscience, and Psychology.

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 will be used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. MTH 245 satisfies the basis requirement for Biological Science, Engineering, Environmental Science, Neuroscience, and Psychology.

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 will be used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. MTH 245 satisfies the basis requirement for Biological Science, Engineering, Environmental Science, Neuroscience, and Psychology.