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.

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

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.

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

You'll see the normal distribution or Bell curve repeatedly throughout this course ah, but that doesn't mean that all variables are distributed normally.

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

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.

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.

And the bottom curve is the weight of a normal mouse.

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.

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

So, if I just did a curve over that and then a curve over that, the whole distribution shifted.

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

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.

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.

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.

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.

Now based on what we know about the normal distribution and we're assuming this normal distribution, we can estimate the probability with which you will make one of these errors.

That's actually what I do in the other video, just to approximate the area under the curve and give you a good sense that the normal distribution is what the binomial distribution becomes essentially, if you have many trials.