Applied Statistics 363 Quantitative Political Methodology

This is an introduction to research methodology and quantitative analysis for social scientists. Students will be introduced to the logic of social scientific inquiry, and to the basic statistical tools used to study politics. Students will learn and apply the following to answer substantive questions: measurement, descriptive analysis, correlation, graphical analysis, hypothesis testing, confidence intervals, analysis of variance, and regression analysis. Major components of the course include learning how to collect, manage, and analyze data using computer software, and how to effectively communicate to others results from statistical analyses. Students will work collaboratively on research projects where they pose their own questions, design a study, collect and analyze the data, and present their findings in a research paper. Additional work may be required for graduate students.

Applied Statistics 2200 Elementary Probability and Statistics

An elementary introduction to probability and statistics. Discrete and continuous random variables, mean and variance, hypothesis testing and confidence limits, nonparametric methods, Student's t, analysis of variance, regression and contingency tables. Graphing calculator with statistical distribution functions (such as the TI-83 series) is required. Prerequisite: Math 131.

Applied Statistics 364 Intermediate Applied Statistics: Linear Models

This course provides a detailed introduction to linear statistical models, the workhorse of applied statistics. Building on foundations in basic probability theory and central limit theorems, linear statistical models can be built from first principles to describe data and/or formalize statistical inference. The remainder of the course will evaluate the robustness to deviations from the basic assumptions and thus generalize the core principles of the linear regression model. Prerequisites: Applied Statistics 330.

Applied Statistics 413 Introduction to Econometrics

Course provides a basic working knowledge of econometrics. Topics include: translation of economic theory into statistical models, statistical foundations of econometrics, preregression analysis bivariate and multiple regression techniques, hypothesis testing, multicollinearity, specification error, autocorrelation, errors in variables, identification, and simultaneous estimation. Prerequisite, Econ 103B and 104B and Math 2200 or equivalent.

Applied Statistics 321G Philosophy of Science

What is science? To what extent is it objective? How are scientific theories constructed? How are they confirmed? Other topics include induction and probability, application of statistics, the status of theoretical entities, and scientific revolution.

Applied Statistics 420 Categorical Data Analysis

This course represents an introduction to methods for analyzing categorical data. Methods covered include those for contingency-table data (i.e., all variables are nominal or ordinal), as well as regression models for nominal and ordinal outcome variables. Although distribution theory and maximum likelihood are introduced as needed, the emphasis is on learning when and how to apply the methods, and how to interpret the results. Computations will be done either by hand or with the SAS computer program. Previous experience with SAS is useful, but not assumed. PREREQ: ASTAT 350 or equivalent.

Applied Statistics 430 Multilevel Modeling

Multilevel models (also called hierarchical, random-effects, and mixed-effects models) are an increasingly important statistical tool in many social sciences. Examples include education (data on students within schools), economics (panel data), political science (data characterized by states and years), law (police stops categorized by date, location, and ethnic group), medicine (meta-analysis), public health (small-area estimation), social work (studies of individuals within housing areas), and many other areas.

This course covers setup, inference, and checking the fit of multilevel models. Computation using the software packages R and Bugs and applications in social science and elsewhere. By the end of the course, you should be able to understand multilevel models and apply them creatively to your data-analysis problems. PREREQ: ASTAT 350 or equivalent.

Applied Statistics 440 Factor Analysis and Related Methods

In factor analysis, a "factor" is an unobservable construct hypothesized to give rise to observed variables (e.g., responses to questionnaire items). This course introduces popular factor-analytic models and methods for fitting them to data, in both exploratory and confirmatory contexts. Models for (approximately) continuous observed data are covered, as well as those for categorical observed data, including a few models and methods of item response theory. Application and interpretation are emphasized, with statistical theory introduced as needed. Use of one or more computer programs will be required (prior experience with factor-analytic software is useful but not assumed). PREREQ: ASTAT 350 or equivalent.

Applied Statistics 450 Panel Data

This course examines the significant statistical issues related to the analysis of panel data. Panel data can be generically described as containing multiple units observed at multiple points in time. Because panel data require attention to both heterogeneity and dynamics, we will cover both topics individually, in summary form, before considering their interaction and developing intuitions for situations that require greater attention to one than the other. Though a host of other topics will receive attention, we will focus on the following issues: (1) Can individual time series be pooled and under what conditions? (2) Deterministic vs. random sources of variation arising from units or time points; and (3) What issues arise in translating techniques for panel data to censoring, truncation, and other pathologies that result in limited dependent variables? Prerequisites: Intro to Applied Statistics (330/513), Intermediate Applied Statistics - Linear Models (350/515), the equivalent, or permission of the instructor.

Applied Statistics 560 Visiting Scholar Statistical Research Seminar in Applied Statistics

This course brings distinguished academic statisticians to WU as part of an organized research seminar. Lacking a Statistics Department or Ph.D. program in statistics, the campus community can substantially benefit from internationally recognized scholars in the field who are willing to spend substantial time at the university. Selected statisticians will come to campus twice during the course. First, they will spend two days at the beginning of the semester to introduce a research topic in statistics and to assign a reading list of 8-12 technical papers, including some of their own authorship. Second, they will return to campus towards the end of semester for fur day for: two 2-hour seminars, a scholarly talk in the Center for Applied Statistics, and individual meeting time with seminar participants and other members of the university community. In-between these two visits, a faculty member in the Center for Applied Statistics will lecture and lead a discussion on each of these assigned papers as part of the weekly seminar meeting. The objective is to provide deep understanding of a complex technical topic through the use of experts in the field. PREREQUISITES: Math 439 or Political Science 581 or Biostat L24-439 or Econ 413, or approved equivalent.

Applied Statistics 581 Quantitative Political Methodology I

Continuation of Psych 5066. Introduction to multiple regression/correlation analysis. Topics include bivariate and multiple correlation and regression, representation of nominal or qualitative variables, power and orthogonal polynomials, interactions, analysis of covariance, repeated measures design. PREREQ: Psych 5066.

Applied Statistics 5067 Quantitative Methods II

Continuation of Psych 5066. Introduction to multiple regression/correlation analysis. Topics include bivariate and multiple correlation and regression, representation of nominal or qualitative variables, power and orthogonal polynomials, interactions, analysis of covariance, repeated measures design. PREREQ: Psych 5066.