# Courses

The following course descriptions and prerequisites have been taken from the Vassar Course Catalog.  In addition, for each course, we have listed other courses for which it is a prerequisite and other courses with which there is substantial overlap in content.  The courses are listed in increasing order of course number.

Math 141 / Biology 141: Introduction to Statistical Reasoning

The purpose of this course is to develop an appreciation and understanding of the exploration and interpretation of data. Topics include exploratory data analysis, basic probability, design of studies, and inferential methods including confidence interval estimaation and hypothesis testing.  Applications and examples are drawn from a wide variety of disciplines. When cross-listed with biology, examples are drawn primarily from biology. Statistical software is used.  Computationally less intensive than MATH 240.

• Prerequisite: three years of high school mathematics.
• Courses for which this is a prerequisite: Math 242
• Courses with substantial overlap: Math 240
• Special Notes: Not open to students with AP credit in statistics or students who have completed Economics 209 or Psychology 200.  Students who have had calculus should take Math 240.

Math 240: Introduction to Statistics

The purpose of this course is to introduce the methods by which we extract information from data.  Topics are similar to those in MATH 141, with more coverage of probability and more intense computational and computer work. Ming-Wen An, Jingchen Hu.

• Prerequisite: Math 126 and Math 127
• Courses for which this is a prerequisite: Math 242
• Courses with substantial overlap: Math 141/Biol 141
• Special Notes: Not open to students with AP credit in statistics or students who have completed Economics 209 or Psychology 200.

Psyc 200: Statistics & Experimental Design

An overview of principles of statistical analysis and research design applicable to psychology and related fields. Topics include descriptive statistics and inferential statistics, concepts of reliability and validity, and basic concepts of sampling and probability theory. Students learn when and how to apply such statistical procedures as chi-square, z-tests, t-tests, Pearson product-moment correlations, regression analysis, and analysis of variance. The goal of the course is to develop a basic understanding of research design, data collection and analysis, interpretation of results, and the appropriate use of statistical software for performing complex analyses. Ms. Andrews, Mr. Clifton, Ms. Trumbetta, Ms. Zupan.

• Prerequisite: Psyc 105 or 106
• Courses for which this is a prerequisite: All Psyc Research Methods courses, including Cogs 219
• Courses with substantial overlap: (None)

Psychology Research Methods Courses (209 Social Psychology, 219 Cognitive Science, 229 Learning and Behavior, 239 Developmental, 249 Physiological, 259 Personality and Individual Differences): These courses all have Psyc 200 as a prerequisite along with a content course in the relevant area of psychology, and all involve learning how to select, carry out, and write up statistical analyses applied to class-generated empirical data.

Econ 209: Probability & Statistics

This course is an introduction to statistical analysis and its application in economics. The objective is to provide a solid, practical, and intuitive understanding of statistical analysis with emphasis on estimation, hypothesis testing, and linear regression. Additional topics include descriptive statistics, probability theory, random variables, sampling theory, statistical distributions, and an introduction to violations of the classical assumptions underlying the least-squares model. Students are introduced to the use of computers in statistical analysis. The department.

• Prerequisite: Econ 100 or 101 or 102 or permission of the instructor
• Courses for which this is a prerequisite: Econ 210
• Courses with substantial overlap: Math 241

Econ 210: Econometrics

This course equips students with the skills required for empirical economic research in industry, government, and academia. Topics covered include simple and multiple regression, maximum likelihood estimation, multicollinearity, heteroskedasticity, autocorrelation, distributed lags, simultaneous equations, instrumental variables, and time series analysis. The department.

• Prerequisite: Econ 209 or an equivalent statistics course
• Courses for which this is a prerequisite: Econ 310
• Courses with substantial overlap: (None)

Geog 230: Geographic Research Methods

How do we develop clear research questions, and how do we know when we have the answer? This course examines different methods for asking and answering questions about the world, which are essential skills in geography and other disciplines. Topics include formulation of a research question or hypothesis, research design, and data collection and analysis. We examine major research and methodological papers in the discipline, design an empirical research project, and carry out basic data analysis. We review qualitative approaches, interviewing methods, mapping, and quantitative methods (data gathering, descriptive statistics, measures of spatial distribution, elementary probability theory, simple statistical tests) that help us evaluate patterns in our observations. Students who are considering writing a thesis or conducting other independent research and writing are encouraged to take this course. Ms. Zhou.

• Prerequisite: None
• Courses for which this is a prerequisite: None
• Courses with substantial overlap: None

Math 241: Probability Models

This course in introductory probability theory covers topics including combinatorics, discrete and continuous random variables, distribution functions, joint distributions, independence, properties of expectations, and basic limit theorems. The department.

• Prerequisite: Math 122 or 125 or permission of the department
• Courses for which this is a prerequisite: Math 341
• Courses with substantial overlap: Econ 209

Math 242: Applied Statistical Modeling

Applied Statistical Modeling is offered as a second course in statistics in which we present a set of case studies and introduce appropriate statistical modeling techniques for each. Topics may include: multiple linear regression, logistic regression, log-linear regression, survival analysis, an introduction to Bayesian modeling, and modeling via simulation. Other topics may be substituted for these or added as time allows. Students will be expected to conduct data analyses in R. The department.

• Prerequisite: Math 141 or permission of the instructor
• Courses for which this is a prerequisite: (None)
• Courses with substantial overlap: (None)

Econ 310: Advanced Topics in Econometrics

Analysis of the classical linear regression model and the consequences of violating its basic assumptions. Topics include maximum likelihood estimation, asymptotic properties of estimators, simultaneous equations, instrumental variables, limited dependent variables and an introduction to time series models. Applications to economic problems are emphasized throughout the course. Mr. Ruud.

• Prerequisite: Econ 210 and Math 122 or equivalent.
• Courses for which this is a prerequisite: (None)
• Courses with substantial overlap: (None)

Math 341: Mathematical Statistics

An introduction to statistical theory through the mathematical development of topics including resampling methods, sampling distributions, likelihood, interval and point estimation, and introduction to statistical inferential methods. The department.

• Prerequisite: Math 220 and 241
• Courses for which this is a prerequisite: Math 342
• Courses with substantial overlap: (None)

Math 342: Applied Statistical Modeling

For students who have completed Math 341. Students in this course attend the same lectures as those in Math 242, but will be required to complete extra reading and problems. The department.

• Prerequisite: Math 122 or 125, and Math 341
• Courses for which this is a prerequisite: (None)
• Courses with substantial overlap: (None)

Math 347: Bayesian Statistics

An introduction to Bayesian statistics. Topics include Bayes Theorem, common prior and posterior distributions, hierarchical models, Bayesian linear regression, latent variable models, and Markov chain Monte Carlo methods. The course uses R extensively for simulations. The department.

• Prerequisite: Math 220, Math 221, and Math 241
• Courses for which this is a prerequisite: (None)
• Courses with substantial overlap: (None)