It's the quadratic deviation from the mean.

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.

Remember, every sample statistic is just a point estimate.

The variance by its very nature computes in the quadratic.

Now what happens when you change the standard deviation.

We have very good data since 1962 1960 about on the size of families in all countries.

There's variance in the sample SD squared.

So the mean for this sample is 5.47, the mean for the population was 5.5.

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

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.

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

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.

So this is basically the average squared error as measured on your test set.

Consisting of accidental and systematic errors.

Mu is the mean, or the average.

Average

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

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

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

Variance

Error diffusion

Mean

Average Score

Average bitrate

By average

Average value

Mean Score

Average bitrate

Average bitrate

Average Bitrate

Mean

Notice that this method will also reveal the bias in the wand measurement.

looked at different types of histograms to get the feel of different shapes of distributions.

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.

So, intuitively speaking, the formulas are really easy.

Monochrome error diffusion

Having done that, finally, what

And to make some of our, make the math a little bit easier, I'm going to actually look at, you know, 1 over M times that.

T statistic from this.

You evaluate it at plus 5, which is right here.