208. Python for Political Scientists, Spring 2013 Recap 209. Evaluating Scripting Languages: How Python Can Help Political Methodologists 210. A Byte of Python 211. 11 Python Libraries You Might Not Know 212. Seaborn: statistical data visualization 213. Beers for Citations 214. 29 common beginner Python errors on one page
Category: statistics
Potpourri: Statistik #27
198. R Markdowns 199. The Quartz guide to bad data 200. Statistikere dybt uenige: Får sorte fodboldspillere flere røde kort end hvide? 201. Bayesian Inference: an interactive visualization 202. Understanding Bayes: Evidence vs. Conclusions 203. A Bayesian Model to Calculate Whether My Wife is Pregnant or Not 204. Not Even Scientists Can Easily Explain P-values […]
Potpourri: Statistik #26
187. The fallacy of placing confidence in confidence intervals 188. Soc 880: Data Visualization 189. Miriah Meyer’s Visualization course 190. Data Analysis and Visualization Using R 191. Checking your Stats, and Some Errors we Make 192. Multiple hypothesis testing 193. How to p-hack 194. A Practical Guide to Regression Discontinuity Designs in Political Science 195. […]
Potpourri: Statistik #25
181. Controlling for confounding variables in correlational research: Four caveats 182. 10 Things to Know About Covariate Adjustment 183. What Does Probability Mean in Your Profession? 184. Trumping Bonferroni to keep your ANOVAs honest 185. Graph and chart aesthetics for experts and laymen in design: The role of familiarity and perceived ease of use 186. […]
Potpourri: Statistik #24
175. Science Isn’t Broken 176. Real Chart Rules to Follow 177. Bayesian Statistics: Why and How 178. Tufte in R 179. Understanding Statistical Power and Significance Testing 180. Why econometrics teaching needs an overhaul