Examples of using Squared error in English and their translations into Polish
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We have the squared error of the line.
Sometimes, it's called the squared error.
Or total squared error with the line.
And so you can even visualize it the same way we visualized the squared error from the line.
So this is the squared error of the line.
So I want to find these two things that define this line. So that it minimizes the squared error.
The total squared error with the line is 2.73.
This is going to be the error with the line. Let me caught it the squared error with the line.
If the squared error of the line is really small what does that mean?
Let me call this the squared error from the average.
If the squared error of the line is small, it tells us that the line is a good fit.
So what is the total squared error with the line?
So are squared error versus our line, our total squared error, we just computed to be 2.74.
Maybe I will call this the squared error from the mean of y.
And so if the squared error of the line is large, this whole thing's going to be close to 1.
Note that other distortion measures can also be considered, although mean squared error is a popular one.
And we want the squared errors between each of the points of the line.
Stein's example(or phenomenon or paradox), in decision theory and estimation theory, is the phenomenon that when three or more parameters are estimated simultaneously, there exist combined estimators moreaccurate on average(that is, having lower expected mean squared error) than any method that handles the parameters separately.
And what we want to do is minimize this squared error from each of these points to the line.
If the squared error of the line is huge, then that means there's a lot of error between the data points and the line.
Tukey's biweight(also known as bisquare)function behaves in a similar way to the squared error function at first, but for larger errors, the function tapers off.
So let me define the squared error against this line as being equal to the sum of these squared errors. .
So if you want to know what percentage of the total variation is not described by the regression line,it would just be the squared error of the line, because this is the total variation not described by the regression line, divided by the total variation.
For squared errors, ρ( x){\displaystyle\rho(x)} increases at an accelerating rate, whilst for absolute errors, it increases at a constant rate.
So if you wanted the total error, if you want the total squared error-- this is actually how we started off this whole discussion-- the total squared error between the points and the line, you literally just take the y value each point.
So if we take 1 minus the squared error between our data points and the line over the squared error between the y's and the mean y, this actually tells us what percentage of total variation is described by the line.
Possibility of choosing one out of three quality regulation criteria 2… 5% readjustment 20% readjustment minimum integral from a square regulation error or Ziegler-Nichols.