And so, you know, the variance, I'm gonna estimate as just the average of the square differences, between my examples, minus the mean.

Now what happens when you change the standard deviation.

I've got in my example minus the mean, minus the center, minus the mean of distribution.

It's the quadratic deviation from the mean.

It should also produce descriptive statistics that look like this. Again, these are from

Mean of the distribution

And I'll just call this, what's the probability that I land between 1 standard deviation the mean is minus 5 one standard deviation below the mean is minus 15 and one standard deviation above the mean is 10 plus minus 5 is 5.

From the mean to one standard deviation above, and one standard deviation below.

I know that's a mouthful. So, you might wanna replay that.

These were the results for Jeremy Lin.

Test are just the average squared error, or one half of the average squared error on my training validation and test sets without the extra regularization chart.

There's variance in the sample SD squared.

Linear middle of the distribution

Let's practice this. I'll give you a couple of quizzes now.

What are the mean and standard deviation of this distribution of the distance between the initial position and the final position?

To show ourselves this, let's just redo the calculation.

Mean value of the normal distribution

Since on average it didn't move, we just add the mean of its starting position to the no movement, and we get a mean of 0.

Here is an interesting data sequence 3, 3, 3, 3, 3.

In particular, consider a single data item as our sequence.

They'll fluctuate, right.

If there is a big difference then this is a larger value and we know for the day in question what the mean and the standard deviation is for this percentage change.

MV is the linear middle of the distribution.

It's one standard deviation in the distribution of sample means. So this distribution of sample means, again.

Mu is the mean, or the average.

Remember, every sample statistic is just a point estimate.

Average

Z scores, then we know what percentage of scores fall in certain ranges.

So the T statistic that we could derive from that is going to be our mean, 17.17 minus the true mean of our population.

Mean value of the standard logarithmic distribution

To see this, recall that a is equal to y bar b x bar, but for a standard score, zₓ always has a mean of 0 and zᵧ has a mean of 0, because we subtracted out their means.

Can you guess what the effect is of adding 1000 to your salary on the mean and the standard deviation?

The variance by its very nature computes in the quadratic.

This is what factory workers earn in an hour in Indonesia on average, 20 cents.

The three main principles are what we just we worked through.

And now I'm going to be really demanding.

What I want to do in this video is to generalize it.

Mean

Average Score

Average bitrate

By average

Average value

Mean Score

Average bitrate

Average bitrate