US politics has more than a few issues, which have been highlighted by the current situation in Washington DC. The protests and greater political awareness from high school students and young adults is a positive sign for democracy, but it needs to be accompanied by increased rates of voting from this demographic. I thought it would be interesting to explore rates of voting in the US across different demographic groups (age, education, income, race). This data is from the 2016 US presidential election.
Total eligible US voting population was about 224 million in 2016 and the overall rate of voting among this population was 61.4%.
The first graph shows the distribution by age. As we can see, the rate of registration and voting increases with age. It is hard to engage young people to be interested in voting but hopefully they will do so in greater numbers this upcoming election.
There was lots of interest in the calculator to estimate counting time (in English) to one million, one billion and up to one trillion. I decided to do the same for other popular languages (Spanish). Here is the calculator that will calculate how long it takes to count to one million (or larger numbers) in Spanish.
Updated: Lots of folks on Reddit pointed out some mistakes in the Spanish calculations, and helped me figure out the solutions, so the Spanish graphs are now updated. The Spanish calculator is now live!
Building off of the last post about Counting to One Million in English, I received some comments about looking at other languages. That seemed like a very good idea, so I looked at a list of the world’s most popular languages and saw Chinese and Spanish listed with English in the Top 3. Having a little experience with both of those, I set out to compare how long it’d take to count in each of these languages, if you had to pronounce every single number from one to one million.
Again, here’s the plot of the number of syllables per number for English. The longest word is seven hundred seventy seven thousand seven hundred seventy seven (20 syllables).
It’s March and for those who follow sports, that means college basketball and March Madness. The tournament is mainly interesting because of two reasons: (1) filling out brackets and (2) watching and hoping for upsets . This interactive March Madness matchup visualizer helps do both of these things by showing you the history of the tournament (since 1985 when the tournament expanded to 64 teams) in terms of matchups between teams with different seeds (1 through 16 in four regions).
Tesla has been building innovative and industry-leading battery-powered cars for about a decade, starting with the Roadster, and then the Model S and Model X. The company unveiled the Model 3 (their first mass-market electric car with 220 miles of range, priced at “$35,000”), in early 2017 and hundreds of thousands of people put down a $1000 deposit within a few days. Overall, the number of these pre-orders total about half a million! Impressive for a car most people have not driven or even seen.
The company also had optimistic timeframes for producing and shipping these vehicle: they had originally estimated production rates of 5000 cars/week by the end of 2017 and 10,000 cars/week(!) in 2018. That’s Civic or Camry levels. These have since been delayed due to reports of “production hell” in scaling up mass production for the vehicles. Given the unprecedented demand and production challenges as Tesla transitions from niche automaker to mass-market production, I thought it would be worthwhile to track the sales of Model 3s as they are built and shipped to customers with the Model 3 Sales Tracker.