Download Syllabus, HS510a Applied Design and Analysis Spring 2017 Time

yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts

Numerical weather prediction wikipedia , lookup

Computer simulation wikipedia , lookup

History of numerical weather prediction wikipedia , lookup

General circulation model wikipedia , lookup

Vector generalized linear model wikipedia , lookup

Predictive analytics wikipedia , lookup

Regression analysis wikipedia , lookup

Generalized linear model wikipedia , lookup

Syllabus, HS510a
Applied Design and Analysis
Spring 2017
Time: Tu&Th 5:30-6:50pm,
Location: Schneider Building, Room G-1
Instructor: Grant A. Ritter
Office: Heller Rm 268
Phone: 781-736-3872
Office Hours: 4:00pm-5:30pm Tu&Th
Email: [email protected]
Text: Wooldridge, Jeffrey M. Introductory Econometrics: A Modern Approach 5th ed., Cengage Learning,
ISBN 978-81-315-24-65-7
Prerequisite: Knowledge of basic statistics and use of statistical software (such as HS404 or its
Course Objectives: Course continues a presentation of quantitative methods covering experimental
design issues, statistical analyses, and other topics relevant to researchers in the social sciences.
Course Requirements: The course will include four problem sets to be solved using a statistical software
package, plus a set of five writing assignments which together will form the framework for a proposed
research project. As the Final, the student must combine the five writing assignments together and edit
to produce a potential research proposal. The course is graded pass/fail. To pass the student must
regularly attend class and turn in both problem sets and written assignments.
Outline of Topics (26 classes of one hour twenty minutes each):
Linear Regression Topics
Review of Probability; mean, variance, standard deviation; random variable, independence, correlation
Review of Statistics; population vs sample, sample mean, sample variance, The Central Limit Theorem
Designs for Social Science: experimental, quasi-experimental, or observational
Data Preparation and Preliminary Analyses
Bivariate Analyses
Linear regression models; OLS; reading Stata output
Interpretation of linear regression output
Inclusion of Interaction terms in linear regressions; interpretation of their estimates
Additional Diagnostics for Linear Regression Models: Goodness of fit, VIFs, tests on the residuals
The F-test for comparing nested linear regression models
Multiple Comparison Tests: Bonferroni, Dunnett, Tukey
The Chow Tests
The Linear Probability Model
Logistic Regression Topics
Introduction and Background for dichotomous dependent variable
Graphic representation of relevant empirical data
Modeling the ‘log odds’ – justification for using logit transformation
Fitting the model, Intro to maximum likelihood method of solution
Interpretation of the model estimates – the odds ratios, constructing confidence intervals
Calculating marginal effects in logistic regression
Further topics in model building – interaction terms, adding blocks of variables, comparing results
Interpreting the interaction term in logistic regression models
Assessing model fit and comparing among models - 2LLN versus AIC versus BIC, pseudo R-square
Pros and cons of logistic modeling versus linear probability modeling
Application to observational, cohort, and case-control study designs
Diagnostics: the ROC curve, concordance and discordance, Somer’s D statistic
Additional Social Science Topics
Poisson, Negative Binomial, ZIP, and ZINB models for counting measures
Mediators and Moderators
Difference in Difference Models
Matching and Propensity Score Matching