Quant Meth 1 Appl Reg Caus Inf · Political Science
I learned how to approach quantitative analysis from a narrative perspective. I learned more R coding, especially base R coding. I learned ways to assess different methods of linear regression and I learned the difference between predictive and causal models.
How to use R; Bayesian statistics; rstanarm
How to correctly interpret linear regression model
Applied regression, causal inference, data simulation
We learned about regression methods in terms of comparisons, to think more deeply about what regression methods are doing, the assumptions underlying these tools, and how to incorporate variation and uncertainty into our analyses.
I learned a bit about Bayesianism, a bit about the short comings of frequentist approaches, and a bit about RStanarm.
How to understand applied statistics at an intuitive and practical level
This course doesn't make much sense to me. Although the professor said that the approach to this course is very radical compared to other schools, I don't think that "radical" means "good" in this case. On the contrary, this course is not useful for students without a basic knowledge of frequency statistics, as it does not properly introduce the basic ideas of statistics and probability. It's also not very helpful for those with some experience since it's not about Bayesian statistics but just about some random comparisons between Bayesian and "Frequentist" (whatever it means) stats. The essence of the course is not clear to me, I do not feel that I am learning anything from it.
Some relevant intuition about statistics and a lot about simulation and how important they are
I would rather suggest taking some introductory course on econometrics or linear regression. I do not think that anyone should start learning stats with Bayesian perspective on it.