So I'm going to compute the average of all the blue points, compute the average of all the red points and move my cluster centroids like this, and so, let's do that again.

That line is broken up into 32 squares.

So in the first exercise, we will calculate the mean of data.

Could Not Calculate Average

And here is the tricky part.

Compute for me the mean and the variance using the maximum likelihood estimator I just gave you.

Could not calculate an average.

3, 9, 3, 8, 2, 9, 1, 9, 2, and 4.

You will see they go between 165,000 and 190,000.

I've ordered it. There's three tails and seven heads.

So let me give you a list of numbers 5, 9, 100, 9, 97, 6, 9, 98, and 9.

So in other words, this number is the average of my first five exams.

There is a little error over here, a little error over here, and here and here.

It has 5 above and 5 below, and that happens to be a 3.

Let me just briefly summarize what we're doing.

This is what you do, for example, when you compute the mean outcome of an election from a sample of size N. You take the sum of the things you saw and you compute the mean.

These are Gaussians with equal variance but different means that might look as follows.

You can do this in two lines.

Linear middle of the distribution

Now with that out of the way ... ... let's figure out what his average velocity was.

So, there are phenomenas that make life difficult for us. Finally, the Pascal

We just want a simple example.

Now assuming you'll use the method I just told you about, and you compute the mean after outlier removal, what do you get?

In assignment 1, I literally want you to flip a coin 1000 times.

Flip itself every single time I run it will give me a 1000 0s or 1s or truths or false and using the function mean, I compute the mean of that.

MV is the linear middle of the distribution.

3, 3, 3, 3, 3, and, I don't know, 100.

This is in thousands.

The first is the one that you're probably most familiar with. It's the one and people talk about hey, the average on this exam or the average height. And that's the arithmetic mean.

You compute this so many times, these are just the numbers, but I want you to give me is the confidence intervals, which is the mean plus or minus the term on the right.

Finally, if you were to calculate the absolute brightness of a grey scale image, or let me call this the average brightness.

And what they do is they average out all of the numbers from all of those analysts.

So if we wanted to compute the clustering coefficient of this graph, we'd actually have to go through node, to node, to node, to node, to node, to node, to node, compute the clustering coefficient for each one and then average.

Here is my solution. Test is a function of the data I and the hypothesis h.

They said, well, how can we do it?

Mean height

Mean height

We removed the outliers using a very context dependent method of just thresholding.

I square those differences, which gives me 4, 1, 0, 1, 4.

Computed Style

4, 3, 32, 33, 4 again, 32 again, 3, 38, and 4.

Three, 4, 5, 6, 7, the mean is 5.

You might now say that I don't know which type you are.

It consists of multiplying the values in the first diagonal and subtracting the product of the second diagonal.

So we have to remove 3 from both sides.