![]() ![]() You'd call it bi-modal,Īnd this makes sense because you have a lot of days that are warm that might Or roughly symmetric, you wanna be more precise, and here when you have these two peaks, that's where the bi comes from. So, even though bi-modal distributions can sometimes be symmetric See these two peaks, this would typically be calledĪ bi-modal distribution. 'Cause if you did that little exercise of drawing a dotted line down the middle, it looks like the two sides are This distribution here, which is telling us the number of days that we had different high temperatures, that this looks roughly symmetric, or actually even looks exactly symmetric. Now, these right twoĭistributions are interesting. A large amount of our dataįell into this right bucket of three cups of coffee, but then we have this tail to the left. Well, this is a very similar situation to what we saw on the dates on pennies. If they found another person who drinks one cup of coffee, that's them, then they found three people who drank two cups of coffee. The office and surveyed how many cups of coffee each person drank, and if they found someone who drank one cup of coffee per day, maybe this would be them. Well, this could be aĭistribution of maybe someone went around Now, if we look at this next distribution, what would this be? Pause this video and think about it. That skews us to the right, this is known as a So, here where the bulk of ourĭistribution is to the left, where we have this tail None of them actually have zero, they all have at least one representative, but they would fall into this bucket, while very few have more The states in the United States have between zero and ten representatives. Of state representatives, and as you can see, most of Now, the other side of a left-skewed, you might say, well, that would be a right-skewed distribution, and that's exactly what Your distribution on the right, but then you have this long tail that skews it to the left. What makes it left-skewed, but the way that you can recognize it is, you have the high points of We'll come up with more technical definitions of And this type of distribution when you have a tail to the left, you can see it right over here, you have a long tail to the left, this is known as a While very few pennies had a date older than 1980 on them. More than 55 pennies, had a date between 20. They saw many pennies, looks like a little bit So, someone went out there, observed a bunch of pennies, looked at the dates on them. Now, here we have a distribution that gives us the dates on pennies. You're collecting data, you'll see roughly Why is it called that? Because if you were to draw a line down the middle of this distribution, both sides look like mirror And if you were to sayīetween five and a half tenths and six tenths, it looks like And then if you say between sixĪnd a half and seven tenths, there's about 30 houseflies. Tenths of a centimeter." Looks like there's aboutĤ0 houseflies there. And then said, "Hey look, there's many houseflies that are between six tenths of a centimeter and six and a half So, someone went out there and measured a bunch of houseflies. So, let's first look at thisĭistribution right over here, it's the distribution of And what we're gonna do with this video is think about how to classify them, or use the words people typically use to classify distributions. ![]() What we have here are six different distributions.
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