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.

Could Not Calculate Average

And here is the tricky part.

Could not calculate an average.

That line is broken up into 32 squares.

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

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

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

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

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

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

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

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

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.

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

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.

We just want a simple example.

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.

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

Let me just briefly summarize what we're doing.

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

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.

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

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

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

So we have to remove 3 from both sides.

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

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.

Let's now go back and write a program in which we calculate the new mean and the new variance term.

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

Our Xi's can now be arbitrary values not just 0s or 1s.

Remember the notation, the sum of squares and mean squares.

I won't let us know that this is equal to that.

I get a parallel data set called ndata that is data minus the mean, squared.

Average

So what we are going to do is look at all the red points and compute the average, really the mean of the location of all the red points, and we are going to move the red cluster centroid there.

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

And the correct answer is minus 0.02708 and 0.0256 and this is obtained by taking the mean over here and adding in the standard deviation plus minus up multiplied by 1.96.