Because we'll see later in this video that to some degree, that assumption is one of the biggest drivers of assumption.

So I'd have to write negative, we're assuming this is going out,

The audio level must not exceed 80 db.

They have all sorts of stuff.

So this spreadsheet, I've plotted the normal distribution.

But that's not the only thing we have on the left hand side we also have these other three kilograms.

Our profit in the first period is 30,000.

let's say they're just really good at their analysis and figuring out market psychology and things like that.

Well, now we got a problem.

It could actually be 90 as advertised and you were just unlucky, but we believe that high probability of this is correct or is the data actually contradicting this claim and you should complain that the claim is incorrect.

So let's for a second, just pick a point on this distribution over here.

And then we observe the value iteration converged and we're able to define a policy if we're assuming the argmax under the value iteration expression, which I don't spell out over here.

If I can reset June's presentation to about 3 or 4 slides ago.

And in the previous videos, the constants all merged together.

When you're done with this, you arrive at a number that now combines the cancer hypothesis with the test result.

Ivan.

So x minus 2, times x minus 1.

So, let's go back to that normal distribution.

Where'd you be, if he took a drop over and above, and had a fancy to go for you?

We are in replay mode, however.

And the way this works, is, in null hypothesis significance testing, which is a mouthful so I'll just say NHST, we assume, the null hypothesis.

Whereas before we measured ranges or distances, now we measure bearings or angles.

The Laplace transform of 1 we just did it at beginning of the video was equal to 1 s, if we assume that s is greater than zero.

Check that box if you think that would be helpful.

Let's work through an example

Here are the outcomes and here are the probabilities.

We know from definition that 1 is equal to i times i. Everything seems pretty straightforward right now. And then they'll say,

John's hypothetical man

And we can turn its heartbeat on and off by going in and out of suspended animation like you would a light switch.

looking at the coin.

So what I did in yellow, both this bright yellow and this

So if you were 10 you know from the beginning to you're either 50 or 10 if you are 10, then when you go to this guy, he would be able to deduce that he is 30.

That's just 1 s.

So I'm going to have to add something like 1 or 2 or whatnot to (x) to get (z).

Let's suppose that he is here.

And in particular I'm going to set up an unfair coin

It could be function of just y.

This is what I assume about annual maintenance.

Let's add another 75 to the 300 in 2003, and we'll get 375.

I'm trying to draw George Washington.

We now come to the hypothesis test unit number 2 and we just put together what we've learned in the previous hypothesis test unit and in the unit on confidence intervals.

I can save on lighting calculations each frame.

Because it is a little bit of a game.

That'll give us 450 students in 2005, if we assume the pattern continues.

Maybe I'll experiment doing it the other way. but let's assume this is g prime of x, and let's assume this f of x.