What does it really mean to control for another factor in a regression? This Python tutorial uses a simulated fast food coupon campaign to show that any such result can be rebuilt by first removing what the other factors explain and then plotting what is left in a simple two-variable chart. It comes with an interactive app that runs in your web browser.
Does working from home make employees more productive, or do productive people simply choose to work from home? This beginner Python tutorial uses simulated data with a known answer to show a four-step approach to cause and effect: state your assumptions, decide what to compare, estimate the effect and stress-test it. It comes with an interactive app that runs in your web browser.
Do carbon emissions first fall, then rise and then fall again as countries get richer? With 12 possible control variables there are thousands of possible models, so we use two data-driven methods that weigh or select the controls and test them on simulated data where we know the right answer. This Stata tutorial comes with an interactive app that runs in your web browser.
What does it really mean to control for a variable in a regression? We use a classic result from statistics to turn that idea into simple scatter plots, first with simulated data and then with real data that follows the same people over time. This R tutorial comes with an interactive app that runs in your web browser.
What does it really mean to control for a variable in a regression? In Stata, we turn that idea into a picture: we strip out the influence of other factors and plot what is left, using examples on store sales, airline flights and worker wages. It comes with an interactive app that runs in your web browser.
When many factors could explain carbon emissions, which ones truly matter? Using simulated data for 120 fictional countries where the right answer is known, this R tutorial compares three methods for choosing which factors to keep and shows that factors flagged by all three are the safest bets. It comes with an interactive app that runs in your web browser.
Do regions with high or low human development cluster together on the map of South America, and did those clusters shift between 2013 and 2019? This Python tutorial maps 153 regions and uses spatial statistics to test whether neighbors are more alike than chance would suggest. It comes with an interactive app that runs in your web browser.
Do poorer districts in Indonesia catch up with richer ones at the same pace everywhere? This Python tutorial uses a map-based regression that lets each relationship change from place to place, each at its own geographic scale, across 514 districts. It comes with an interactive app that runs in your web browser.
How can two health measures, life expectancy and infant mortality, be combined into one fair health score? Using simulated data for 50 countries, this Python tutorial builds the index step by step with principal component analysis, a method that finds the single direction capturing most of the shared information. It comes with an interactive app that runs in your web browser.
How can we tell whether regions are developing over time when the measuring stick itself must stay the same? Using education, health and income data for 153 South American regions in 2013 and 2019, this Python tutorial builds one development index from both years together so that scores can be compared across time. It comes with an interactive app that runs in your web browser.