Re: Questions about square errors

On May 2, 9:38 am, "aggie2525" <aggie2...@xxxxxxxxxxx> wrote:
Hi,

I am working on a model that use about 9 input parameters to predict an
output.
Since I have about 800 data sets (each data set has 9 input data and an
output).

I come up with a method that I can predict an output from 9 given input
data.
Then, I use the model that I have to predict each output for each set of 9
input data.
As a result, I have a square error for each prediction.
Therefore, there should be about 800 square errors

My question is if it is OK that I plot all 800 square errors to get their
distribution.
Then, from this distribution curve, I can get a range of errors with a
specified confidential level for my prediction.

My concern is that the square errors would be dependent to certain input
patterns.

Thank you in advance for any help and reply.



If you have minimized the sum of squares of the differences between
observed and predicted values, then it appears you may have invented
least squares and/or multiple regression.

It's smart to examine and study the differences between observed and
predicted values. In multiple regression, those are called residuals.

But there's a lot more to it than this. For instance, it's a good bet
that your nine predictors are to some degree correlated among
themselves. Your model may have several unnecessary predictors. If so,
then the presence of those probably degrades the capability of your
model for predicting future outcomes. The fact that it may predict
existing data fairly well is not necessarily an indicator of how well
it will predict future experiences.

Then there's the matter of the significance (or absence of
significance) of each of the individual predictors and the confidence
intervals on the estimated value of each predictor.

All of these should be taken into account before moving forward.

If you are not familiar with... and skilled in... the analysis of
multivariable data, then I suggest you get some help with this
project. OMU

.

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