Here is a suggestion: In empirical research, academics should move equations from the methods section to the appendix and, if anything, show the few lines of code used to estimate the model(s) in the software being used (ideally with citations to the software and statistical packages). Preferably, it should be possible to understand the estimation […]
Category: statistics
How to improve your figures #7: Don’t use a third dimension
Most static figures show information in two dimensions (with a horisontal dimension and a vertical dimension). This works really well on the screen as well as on paper. However, once in a while you also see figures presenting figures with a third dimension (3D). It is not necessarily a problem adding a third dimension if […]
How to improve your figures #6: Don’t use bar graphs to mislead
In a previous post, I argued that the y-axis can be misleading under certain conditions. One of these conditions is when using a bar graph with a non-zero starting point. In this post I will show that bar graphs can be misleading even when the y-axis is not misleading. In brief, bar graphs do not […]
Visualizing climate change with stripes
Climate change is abstract. We do not personally experience climate change in our day-to-day activities (although cimate change is detectable from any single day of weather at global scale, cf. Sippel 2020), and if we are to understand climate change, data – and in particular data visualisation – is crucial. I have recently been reading […]
Potpourri: Statistics #75
905. Introducing pewmethods: An R package for working with survey data 906. Exploring survey data with the pewmethods R package 907. Weighting survey data with the pewmethods R package 908. Analyzing international survey data with the pewmethods R package 909. autumn: Fast, Modern, and Tidy Raking 910. Data science for economists 911. Papers about Causal […]