Dispersion of the normal distribution

This is a normal distribution, here's a cumulative distribution.

Mean value of the normal distribution

Middle value in the normal distribution

Standard deviation of the normal distribution

So, here is our normal distribution again.

So first let's revisit this normal distribution.

That's a uniform distribution. Red Truck is showing this nice normal distribution.

But still we had a nice, normal distribution.

So here we're looking at the normal distribution.

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

What we would observe is a normal distribution.

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

Value to which the standard normal distribution is calculated

When you actually use the Excel normal distribution function,

We're going to use that normal distribution a lot.

This is just a normal distribution of body temperatures uh, so it's relatively normal.

Finally the shape of this distribution of sample means will be approximately normal even if the distribution in the population isn't normal.

So, the most important concepts in this segment are again to think about the normal distribution and the properties of the normal distribution.

This tells you the height of the normal distribution function.

And we had a nice normal distribution of body temperature.

Again, we use that normal distribution to estimate those probabilities.

So a T distribution looks very similar to a normal distribution but it has fatter tails.

So, what we're seeing here in the, in the male, distribution, is, also a normal distribution.

Let's be safe and assume 30. You'll get a normal distribution in your distribution of sample means.

And what you see here is just a very normal distribution, has a sort of a high peak ah, relative to the standard normal distribution.

That means somebody in the distribution is not normal. They're sick.

The normal distribution is arguably, the most important concept in statistics.

This is a roughly normal scan, showing equal distribution of energy.

We'll be out in the extreme. Ranges of this normal distribution.

Say we want to deal with a joint probability distribution, say the distribution of heads and tails over these 2 coins.

So, we know certain properties about the normal distribution, if it's perfectly normal, and if it's standardized.

Okay, so remember, for Woop, Woop, we had, a uniform distribution. Red Truck, we had a normal, distribution.

In this video, I'd like to talk about the Gaussian distribution, which is also called the normal distribution.

The number for which the standard normal distribution is to be calculated

That's the purpose of normal distribution for the sake of this class.

So let's bring brack, bring back that graphic of the normal distribution.

Joint

From that, we arrived with the normal distribution which is basis to so much in statistics all of testing and confidence in results are defined though the normal distribution.

So, the answer is going to be a complete, joint probability distribution over the query variables.

The definition of this joint distribution by using the following factors has one really big advantage.

That is, with an infinite number of samples, this procedure computes the true joint probability distribution.

This end and this end will be fatter than in a normal distribution.

With that said, let's play around a little bit with this normal distribution.

Anyway, hopefully that will give you a good feel for the normal distribution.