Regression analysis is an enormously popular and powerful tool, used ubiquitously in the social and behavioral sciences. Most courses on the subject immediately dive into the mathematical aspects of the subject and illustrate the technique on problems that are already highly structured. As a result, most students come away with little idea of the wide range of problems to which regression analysis can be applied and how to represent those problems in a way that cleverly utilizes readily available data. Few understand, at a conceptual level, the limitations of regression analysis. The OLI Empirical Research Methods course bridges the gap between the mathematical foundations of regression and its practical application. We teach students how to move from an interesting question about the world to a regression model that, when estimated, meaningfully addresses the question asked. It emphasizes causal analysis as the main research goal and multivariate linear regression as the main statistical tool. We teach a process that involves: Formulating a research problem, Developing and formalizing hypotheses, Collecting data relevant to these hypotheses, Analyzing the data using an appropriate regression model, and Critically interpreting the results of these analyses.
This course is a self-contained introduction to statistics with economic applications. Elements of probability theory, sampling theory, statistical estimation, regression analysis, and hypothesis testing. It uses elementary econometrics and other applications of statistical tools to economic data. It also provides a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed in the further study of econometrics and provide basic preparation for 14.32. No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed.
This course is a self-contained introduction to statistics with economic applications. Elements of probability theory, sampling theory, statistical estimation, regression analysis, and hypothesis testing. It uses elementary econometrics and other applications of statistical tools to economic data. It also provides a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed in the further study of econometrics and provide basic preparation for 14.32. No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed.
" This course will provide a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed for further study of econometrics and provide basic preparation for 14.32. Topics include elements of probability theory, sampling theory, statistical estimation, and hypothesis testing."
The course introduces statistical theory to prepare students for the remainder of the econometrics sequence. The emphasis of the course is to understand the basic principles of statistical theory. A brief review of probability will be given; however, this material is assumed knowledge. The course also covers basic regression analysis. Topics covered include probability, random samples, asymptotic methods, point estimation, evaluation of estimators, Cramer-Rao theorem, hypothesis tests, Neyman Pearson lemma, Likelihood Ratio test, interval estimation, best linear predictor, best linear approximation, conditional expectation function, building functional forms, regression algebra, Gauss-Markov optimality, finite-sample inference, consistency, asymptotic normality, heteroscedasticity, and autocorrelation.
No restrictions on your remixing, redistributing, or making derivative works.
Give credit to the author, as required.
Your remixing, redistributing, or making derivatives works comes with some
restrictions, including how it is shared.
Your redistributing comes with some restrictions. Do not remix or make
derivative works.
Copyrighted materials, available under Fair Use and the TEACH Act for US-based
educators, or other custom arrangements. Go to the resource provider to see
their individual restrictions.
