Plotting just your data often helps you gain insight into how it has changed over time. But what if you want to know why it changed? Although correlation does not always imply causation, it is often useful to graph multiple things together, that might logically be related. For example, recessions
Many areas of the US are experiencing record low unemployment. This is great at the national level, and also great at a personal level (for example, I now have fewer unemployed friends asking to borrow money!) But just how low is the US unemployment rate, and how does it compare
It looks like we've finally recovered from the Great Recession, and there are even claims of record-low unemployment in several U.S. states. Of course claims like that make my data-radar go off, and I wanted to see the numbers for myself. And it's a great excuse for me to create
The US unemployment rate was down to 4.4% in April, which is the lowest we've seen since before the big recession (about 10 years ago). But a single number seldom tells the whole story, so let's look at unemployment data in several different ways, to get a more complete picture...
Can the selection of the axis range in a graph influence how you perceive the data? Let's find out with a "Labor Participation Rate" graph ... Medical doctors have traditionally taken the Hippocratic Oath, swearing to practice medicine honestly. I have often thought that people creating graphs should swear a
Which would you rather see - a table of numbers, or a nice graph? When it comes to unemployment numbers, I vote for the graph! The Bureau of Labor Statistics (BLS) provides several tables of data about the U.S. workforce. One such table (Table A-15) provides several different alternative measures