For this run the mean was 0.50006.

I repeated this experiment 1000 times and that means

And it contributes to about 70 samples of those thousand samples.

Well, the tissue sample yielded unique chemical compounds during the cremation that revealed the nature of the samples to be synthetic.

It's also called the zero one misclassification error, with zero one denoting that you either get an example right or you get an example wrong.

So, it's a point estimate meaning it only represents that sample.

And in about half an hour, we got five samples.

This has inspired the design of the first self cleaning dry adhesive the patent issued, we're happy to say.

So, to review this segment on Histograms, the important concepts to take away are just one, what do I mean when I say a distribution?

That if I. Do an experiment over and over again if I get repeated samples.

We have our sample mean and our sample standard deviation, our sample mean here is 17.17 figured that out in the last video, just add these up, divide by 10 and our sample standard deviation here is 2.98.

Tons of studies should have been done it's a serious issue.

Here, I said samples equal to 30 or greater, some people would be a little more liberal then that and say if you have samples of even twenty or greater you'll get a normal distribution.

If you add those together, we get 1.2.

Is this an anomaly or not?

And then we evaluate it.

So plus another 4 centimeters.

Minus 2 times minus 1, that's plus 2, is equal to c.

So by taking this sample you can say that we've randomly sampled a T statistic from this 9 degree of freedom

And in three minutes, we filled in another table in our

Not because you had a strong regression co efficient, just because you had a large sample.

There may be some random variation that causes them not to converge to their true values, but as we add more counts, the counts as we add more samples, the counts we get will come closer to the true distribution.

And there you go.

That is, we say that A will always be positive, and then we sample the rest of the variables, so then we get samples that we want.

And then minus 56 minus 10, so minus 66.

I know there's a lot of fractions here, but if you just work through this, the fractions actually work out.

Add to the imaginary parts you have 5 i minus 6 i. You get negative i

The vessels looked better.

So we're in this scenario right over here.

And on the X axis here are examples.

From k is equal to 0, to n of a to the k.

We figured out what f of 0 is up here.

I'm going to multiply 8 times both sides of this equation.

Is that what we got?

Samples. Samples.

Well, just looking at that little example with the wine experts.

So essentially, what did we do here?

So you're going to get some combination of the i and j unit vectors.

The cells looked better.

And you probably know, hey, well, if I take 2 away from 5,

If you would like to have a sample, please let us know.

like that shown on the screen.

We see our answer is 15, 18, 21.

We're multiplying our f of t times e to the 1t.

Jay, you got them?