The standard error of the estimator in a simple linear
Chapter 12, Problem 8E(choose chapter or problem)
The standard error of the estimator \(\widehat{\beta_{1}}\) in a simple linear regression model gets smaller as \(S_{x x}\) increases, that is, as the -values become more spread out. Why don't we always spread the -values out as much as possible?
Equation transcription:
Text transcription:
widehat{beta{1}}
S{x x}
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