Welcome to Excel 2013 statistical video number 21.
Hey, if you want to download this workbook and follow along,
click on the link below the video.
Hey, we have a quick video here about skew.
Now, wait a second.
We've already talked about skew twice already,
once when it came to histograms and once when it came to mean,
median, and mode.
So here's a histogram that shows some small values to the left.
Here's a histogram that kind of looks bell shaped.
Here's a histogram that has some big values, just a few of them,
up on the right.
So his histograms help us see visually
with a graphical method what the skew is.
And when we talked about mean, median, and mode,
we noticed that most of the time,
if you have a skew to the left, the mean will be the smallest,
then the median, then the mode.
If it's bell shaped, they're all going
to tend to be exactly the same.
And if it's got skewed to the right, or positive,
the mean is going to be bigger than the median and mode.
Now, this is to the right, or positive,
because a few big values up here are
going to tend to make the mean bigger than the median
and mode.
Over here, just a few small values
are going to tend to pull the mean down away from the median
and mode.
Now, we had our graphical method here.
And we can kind of guess that the skew
is negative and positive here with these numerical measures.
But no way, we want an actual skew measure.
And there it is.
That is a big, messy formula.
If we had to do it longhand, oh, forget it.
That is just too difficult. But no way, there's a function.
And luckily, they called it skew.
S-K-E-W is the function we're going to use that does this
formula.
Here's one for the population.
And notice the screen tip here, returns the skewness
of a distribution a characterization
of the degree of asymmetry of the distribution
about its mean.
If we think about it, this is symmetrical, right?
If it's a bell shape, we draw a line down the middle.
And it's the same on both sides.
These are asymmetrical.
Tab, we simply highlight our numbers.
And that's a relative cell reference.
Control Enter, and as we suspected
we see a few small frequency columns down low.
So our skewness measure is negative.
If I copy this over here, skew is looking down
at this data set, which is bell shaped.
Hey, look at that.
The skewness is 0.
Up here, oh, there's a few really big values.
We get a 1.6.
So skewness as a numerical measure
will help us gauge the shape of the distribution
and the asymmetry.
Zero, bell shape.
Positive, skew to the right, or positive.
Negative, skew to the left, or negative.
Now, let's go remind ourselves about some skew examples we've
already seen in the class.
I'm going to click on skew three.
Now, if you hit the F9 key, I have this data randomizing.
But let's look at this here.
This is a distribution of tests.
And for this particular class, there
tended to be a bunch of pretty high scores
and a few low scores.
This would be skew to the left.
Another example of skew to the left, if this
was age and number of people dying, in the younger ages
there's just not many people dying.
As you get above 50, 60, and 70 then
the frequency of people dying would
be in the higher category.
So that would be an example of skew to the left.
Skew to the right down here, if I hit
F9, if we're talking about house prices, probably
most of the houses in most neighborhoods
are going to tend to be in the lower categories.
And there's a few really expensive houses
pulling the mean up, not always the case.
But certainly in many neighborhoods
that skew to the right, or positive.
Another example is department stores.
You know, up in the high dollar amount,
only a few people are going to be buying all their furniture
sets at a department store.
Whereas, most of the people are going to make small purchases.
So an example of skew to the right, or positive.
F9.
An example of a bell shape, or tendency towards bell shape,
would be human height or weights for bags of lettuce or cereal,
or SAT scores.
So skew will be important throughout the class.
All right, next video, we're going
to talk about the amazing z-score.
All right, we'll see you next video.