U 6 was 9.3 .

So let's think about all the factors of negative 60, or 60.

They did much better than 2100. So the distance between.

So in the example we did, h was the difference between our 2 x values.

By the end of colonialism in the 1930s, they were 35 times richer.

He leads it by a length.

There's always a little fluctuation from sample to sample.

So the numerator we can factor.

Remember, every sample statistic is just a point estimate.

It makes all the difference.

He borrowed it to take his nephew for a ride into town.

Yeah, we don't discriminate.

So, I'm gonna introduce a new formula and a new concept here, and one that will be, essential as we move forward in inferential statistics.

The accident happened at that crossing.

The accident happened at that intersection.

This principle of nature being very remote from the conceptions of philosophers,

So, now that we have defined the training validation or cross validation and test sets, we can also define the training error, cross validation error, and test error.

So it's likely that the line, in terms of learning, is pretty flat.

You with me?

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

The unstandardized regression coefficient, B.

Interestingly, it's done a crossover before, but not the crossovers that matter.

It's a ratio of sum relationship that you observed relative to what you would expect due to chance.

I missed her by two days.

I'll take you right to him.

It doesn't matter.

So if I had minus well, 8 and 2 is equal to 16.

Lalo didn't send you?

So of course, the difference between the actual score Y, or observed score Y and predicted score Y is error.

Concretely, for the high bias case, that is, the case of under fitting, what we find is that both the cross validation error and the training error are going to be high.

A program to view the differences between files and optionally generate a diff

Why the difference between 12 and 5 is 7, and the difference between

At the intersection.

Get our predicted score on y, it's a function of the regression constant plus some predictor times its' slope.

And the way we estimate that is to rely on the principles, of the central limit theorem, and calculate standard error, by looking at the standard deviation, in our sample, and the size, of our sample.

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.

U.K., Portugal, USA, Singapore the differences are twice as big.

Vector Difference

So all we're interested in here is comparing the temperature of the beetle against the background.

A common measure of intelligence, IQ, is distributed with a mean of 100 and a standard deviation of 15.

So now, we're experiencing a lot of sampling error because our sample is so small.

How does this relate to word differential ?

It's easy to get lost in the algebra.

This would be intersection of the plane and the bottom cone.