I love the math you did

Happy to hear they're helpful. I keep meaning to go back and link the documents, but haven't gotten to it yet. But here's this one: drive.google.com/file/d/0B9DLhTnz0HZnd0xXZU9RLVNnazQ/view?usp=sharing&resourcekey=0-YtU7YfUMtg8RHafCW1sjqQ

@@melathrop thank you so much for sharing!!! This will help my students!

sometimes.

The second cell, y <= x^2, is shading the area between the curve and the x-axis. The information in braces restricts both the x and y values so that only the region between x = 0 and 2, and above y = 0 is shaded.

you need this for high school stats...

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Maybe, but only if you use more exclamation marks. !!!!11!!1!!!1!11!1!!!1!1

Sorry bruh it was not meant to be seen by you

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Hi

Me to lol

With this table there is the assumption that people are placed in a single category based off their highest level of education. So people in the "college or higher" category would not be counted in the "high school graduate" category, even though they are (most likely) also high school graduates.

In case you still want to know, or somebody else is wondering, here's an explanation. The reason you multiply the integral by 2 becomes obvious if you look at what exactly the Gini Index means. The Gini Index is the area between the two graphs devided by the total area under the y=x graph. If you calculate the integral for y=x for x=0 to 1, you find that it has an area of 0.5. So when you take the area between the two graphs and devide it by 0.5, it's the same as multiplying the area by 2. Hope it helps!

Adding links to the corresponding lecture note files has been on my to do list for a while. In the meantime, here's a link to all the files in one folder: drive.google.com/drive/folders/0B9DLhTnz0HZnUU8wVzJGdTFpMVU. For this video, you're looking for "MATH 141 M3.1 Skewness.pdf".

thank you soo much for taking out the time to reply

It's called StatCrunch (www.statcrunch.com). There's a fee to use it, but it's pretty low. All my stats videos are developed around using this very straightforward, yet powerful, tool.

+Thanh Như Thọ Phạm Well, for numerical distributions you may only want to consider the mean and median as your measures of center. The mode is most useful for categorical data. That said, a left-skewed distribution typically has mean < median < mode, and a right-skewed distribution typically has mode < median < mean. This isn't a rule, though, and exceptions can exist. If you need to make a decision about skewness for your data, I would suggest simply looking at the mean and median (or just the skewness value), and also construct a histogram so you can tell, visually, what's happening with the data.

+melathrop oh thanks for your reply

Happy to hear it helped!