This definition of precision may be distinct to the one the OP is referring to but it ought to be clear now what Efron's definition is and why it is vital to get precise. Now When you've got two techniques that are actual, we could like one more than another if for any self-confidence stage it's the lesser expected width.
$begingroup$ I am introducing to some great answers below that I gave upvotes to. I think there is a bit more that should be reported to fully clear up the summary. I just like the conditions precise and correct as Efron defines them. I gave a lengthy dialogue on this very not too long ago on a unique question. The moderator whuber actually appreciated that answer. I will not likely Visit the exact same duration to repeat that right here. Even so, to Efron precision relates to the confidence stage and correctness of your width or tightness from the interval.
If you don't center $X$ to start with, your squared time period are going to be highly correlated with $X$, which could muddy the estimation from the beta. Centering very first
self confidence interval, making it less precise. Conversely, should you decrease The arrogance degree then you're going to get a narrower
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That ailment number can be very high when variables are measured on scales with disparate ranges. Rescaling will then take up most of the "badness" in $X$ in the scale variables. The ensuing difficulty is going to be much better conditioned. $endgroup$
The initial monotonicity home means that when you minimize The arrogance stage (by rising $alpha$) you have a more accurate (narrower) confidence interval and vice versa
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Fourth, I am a tad concerned about your statement: I actually need/have to conduct a regression Evaluation to discover which products to the questionnaire predict the response to an In general item (pleasure)
If the items were being summed or someway merged for making the general home scale, then regression is not the correct method at all. You almost certainly want variable Investigation. Share Cite
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