Check your residual pressure.

This is the residual error.

Stay on those residual boulders!

That's the residual sums of squares.

It's a little smaller than if you just click at the end points, which gave us a slope of 2, because there is a residual arrow over here, residual arrow over here, residual arrow over here, and a residual arrow over here.

The big residual is always value for money.

So, the residual sum to squares goes down.

There is really no residual error over here.

We did find some residual ionisation traces here.

He's got a small residual limb on one side.

Your appearance now is what we call residual selfimage.

The residual error is the sum of all training examples,

And what is left, as a residual magnetic fields, is Light.

So this is all residual, whatever is left over from systole.

And there's still residual magnetic energy from whatever electrocuted these people.

That residual interaction of the 2 fields, which is Light, is created.

What you see is the residual sums of squares goes down, alright?

We just stay where he can't get us on these residual boulders.

We want to minimize the residual, we want to minimize our prediction errors.

Sums of the squares for the mo del and sums of squares residual.

Standard error is the sum of squared residual divided by M minus two.

If there was a psychotropic compound, we'd be feeling the residual side effects,

In the early universe there will be density fluctuations as residual of quantum processes.

It's still getting some base level oxygen less because of the stenosis and ischemia but it still gets some oxygen.

Remember, we talked about that we were trying to minimize the residual sums of squares.

This point and the regression line, that's the prediction error or residual for that individual.

What we saw in that case, is we had a really big positive residual for

Larry said Homeland sent her over to check for residual breaches fr the firewall attack.

But for now, it's the square root of the sum to squares residual over N 2.

This is an example of linear regression, in which case there is a residual error, and the best fitting curve is the one that minimizes the total of the residual vertical error in this graph over here.

And this data set happens to be fittable by a linear regression model without any residual error.

And of course the difference is the prediction error, or residual. Now, let's put that in matrix form.

Indeed We sent upon them a single Scream thereupon they became like the barrier builder s residual dry trampled hay.

You know what, points A and B are okay, but point C, you still have some residual cancer there.

Now, a word that you might hear in a medical context is stenosis , and the word just refers to the narrowing, usually of a blood vessel.

B sub one is the slope relating X to Y and e is our prediction error or residual. So if

I'd like to find the formula y w1x w0 by minimizing the residual quadratic error as we learned in class in linear regression.

I've suspected due to the size of the residual scoring... and the burn points... that we are dealing with a G4 type explosive.

They all imply some sort of narrowing, or stenosis, of arteries that leads to ischemia, reduced blood flow, so that the heart can't function as well as it otherwise could.

So when this guy is jogging, because he has restricted blood supply because the heart is experiencing ischemia downstream from this stenosis, that's why there's heart failure so not able to deliver

The Gaussian is maximum where this equation is fulfilled, but if the residual is not equal to 0, there is still a probability associated with it.

But, every so often, we come across children who show some residual vision, and that is a very good sign that the condition might actually be treatable.

I'll call that prediction error or residual. So if we go back to one of our examples this was predicting number of runs scored from batting average.

So there is still a residual probability of being in these places over here, but these three bumps together really express our current best belief of where we are.

The word is called here, are loss function and the loss function is the amount of residual error we obtain after fitting the linear function as good as possible.