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UW Statistics Courses
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== Advanced Statistics Courses == There are many useful courses offered by the Center for Statistics in the Social Sciences (CSSS). Most of CSSS classes are applied and will give you a chance to apply the methods that you learn to your own projects. Try to take advantage of these opportunities to make progress on your research. CSSS 509 is an exception, and will be discussed below under econometrics. '''CS&SS504, Applied Regression''' is an applied, but still technical course on regression. It may vary based on who is teaching the class. This will be the default option for [[CDSC]] students. '''CS&SS 560, hierarchical modeling''' is important. Hierarchical models are the bread and butter for working with datasets that have community level variables and individual level variables, or that have longitudinal data. '''CSSS564, Bayesian Statistics for the Social Sciences''' CS&SS 564 is very good. This may vary by the instructor/text, but in 2023 it was taught using R/Jags/Stan with a project and no tests; the content is a nice blend of mathematical and applied perspectives. There are a lot of online resources that accompany the text so you can learn/re-learn the material a few different ways. It's a fair amount of work because you are building familiarity with doing a lot of simulation and digging your hands into how models are working, but the pre-requisites are low; it's not brain-breaking, just some solid grinding and that takes time. Probably easier than 560 because you will review basics of probability, binomial model, etc. from the intro-sequence but in a Bayesian way. That said, the R is a bit more intense in 564 than it is in 560. Taught using mostly base R -- not tidyverse! '''CSSS566, Causal Inference''' CS&SS 566 is good. It covers experimental, instrumental variable, and quasi-experimental designs, structural equation modeling, and DAGs. It takes a relatively philosophical and theoretical approach to causality and shows that common assumptions about causal identification can be wrong (e.g adding a variable to your model ''can'' introduce bias, in theory).
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