Search Results
Search type | Search syntax |
---|---|
Tags | [tag] |
Exact | "words here" |
Author |
user:1234 user:me (yours) |
Score |
score:3 (3+) score:0 (none) |
Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
Views | views:250 |
Code | code:"if (foo != bar)" |
Sections |
title:apples body:"apples oranges" |
URL | url:"*.example.com" |
Saves | in:saves |
Status |
closed:yes duplicate:no migrated:no wiki:no |
Types |
is:question is:answer |
Exclude |
-[tag] -apples |
For more details on advanced search visit our help page |
The error of an estimate or prediction is its deviation from the true value, which may be unobservable (e.g., regression parameters), or observable (e.g., future realizations). Use the [error-message] tag to ask about software errors.
0
votes
0
answers
403
views
The difference between total error, prediction error and fitted error via residual
Fitted error = $E(Y|X)-\hat{Y}$. Further, define residual = $Y-\hat{Y}$. Now, we have $Total \ error = Residual
\ error + Fitted \ error$. … Therefore, the total error can be expressed as $Total \ error = Prediction \ error + 2 \times Fitted
\ error$.
Is the above derivation regarding errors, is right or wrong? …