F. Extract the names of the CEOs with a Total Pay () greater...

f. Extract the names of the CEOs with a Total Pay ($) greater than 15 million and sort it by the Total Pay ($). How many CEO's made more than 15 million? g. Create a histogram for the column Total Pay ($) and comment on its shape and any observations you make from it. Create equal bins of 5 million each. h. Looking at the histogram, how many CEOs made more than $30 million? (Answer the question by getting your answer manually. You'll have to do a bit of arithmetic; feel free to use Python as a calculator.) i. Answer the same question with code. Hint: Use the table method where and the property num_rows. j. Companies pay executives in a variety of ways: directly in cash; by granting stock or other "equity" in the company; or with ancillary benefits (like private jets). Compute the proportion of each CEO's pay that was cash. (Your answer should be an array of numbers, one for each CEO in the dataset.)

Transcript

00:01 All right, so let me upload the question that you gave me, because it's asking you to pick from the pictures which the correct histogram, but you don't have, you didn't give me any histograms to pick from.

00:30 So i can explain to you what it's talking about.

00:38 About.

00:38 So what it, so your histogram, like you're just putting that data in the histogram.

00:44 So maybe your intervals are from zero to four and then five to nine, 10 to 14.

01:01 So then you're just taking that data and putting it in there.

01:05 So how many are from zero to four? so one, two three let's say four point nine nine so one two three three four four five six seven eight nine ten eleven so like you would go up to say this is five this is ten fifteen so you would go up to a little bit so that this is how your histogram would look so then that would represent 11 and so on and so forth and you would do that with the whole thing now once you look at it the data this data is not going to be normally distributed because you have numbers like 26 .14 and 192 .54 or 36 .67 and 20 .48 and 20.

02:29 6 .02 when most of your data is lower, well, actually you have a couple of 20s, but then you have like these high numbers like 192 .54, 7d8 .84.

02:44 So most of your data is below 20 to 30, there's only 130 point something.

02:54 So it's going to be skewed.

02:56 So your data is going to look something like, i don't know.

03:03 And then you've got, you're going to have some way over here.

03:09 So it is going to be skewed.

03:17 But because most of your data is on the left and you have these little bit that's pulling it right, this is called skewed right because the right side is skewing your data.

03:32 So this data is going to be skewed to the right.

03:36 And i know that because of that 192 .54 and that's 78 .84.

03:45 Identify any unusual values around your answer to two.

03:49 So i'm going to go ahead and tell you you're going to have outliers.

03:55 You're going to have outliers at 192 .5 and 78 .84.

04:02 For you to find those outliers, you're going to have to use the formula, the median minus plus and minus the 1 .5 times the iqr.

04:34 I'm sorry it's not the median.

04:37 It's q3 plus 1 .5 times the iqr and q1 minus 1 .5 times the iqr.

04:58 So you'll need to find the iqr.

05:01 I'll find that you have to do your box.

05:03 And whisper, find your median, and go from there.

05:06 So to do that, you need to put them all in order...

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