Boxcar averager
A boxcar averager is like a machine that helps us figure out the average of a bunch of numbers. Imagine you have a lot of chickens and you want to know how much they weigh on average. You could weigh each chicken one by one and add up all the weights, but that would take a really long time! Instead, you could put a bunch of chickens in a big box, and then weigh the box. The weight of the box and the chickens all together will be a bigger number. Then, you can take out some of the chickens, weigh them, and write down their weights. Next, you can put them back in the box and take out some different chickens, weigh them, and write down their weights too. You keep doing this until you have weighed all the chickens. Finally, you add up all their weights and divide by the number of chickens you weighed, and that gives you the average weight of the chickens.
The boxcar averager works similarly, except instead of weighing chickens, it takes a bunch of numbers and finds the average value. It does this by taking a certain number of data points (like 10 or 20) and adding them together. Then, it divides the sum by the number of data points it added together. This gives us an average value for that batch of data points. The boxcar then moves over to the next batch of data points, adds them up and finds an average value again. It keeps doing this until it has gone through all the data points, and then we get an average of all the averages. This is helpful because it can smooth out any bumps or fluctuations in the data and make it easier to see patterns or trends.
The boxcar averager works similarly, except instead of weighing chickens, it takes a bunch of numbers and finds the average value. It does this by taking a certain number of data points (like 10 or 20) and adding them together. Then, it divides the sum by the number of data points it added together. This gives us an average value for that batch of data points. The boxcar then moves over to the next batch of data points, adds them up and finds an average value again. It keeps doing this until it has gone through all the data points, and then we get an average of all the averages. This is helpful because it can smooth out any bumps or fluctuations in the data and make it easier to see patterns or trends.