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From |
"Mike Kim" <kalisperos@gmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: RE: Insignificant coefficient in prediction |

Date |
Wed, 1 Dec 2010 15:41:14 -0600 |

Thanks. I share with you the skepticism on significance. Let me ask this way. Let's say a practitioner wants to find which factor has more impact on sales to make an investment decision: store size or good location. Let's say, a regression model tells that the effect of store size is smaller than the effect of location but highly significant and that the effect of location is larger but insignificant with p>0.5, for example. What should we recommend? To invest in store size or to pick up a better location? I guess he should invest in store size. Then, what implication does it have on prediction using both coefficients? Are these two problems very different ones and should not be mixed? Thanks again. Mike. -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox Sent: Wednesday, December 01, 2010 12:12 PM To: 'statalist@hsphsun2.harvard.edu' Subject: st: RE: Insignificant coefficient in prediction You get what you ask for. If you fit a model and then follow with -predict-, then Stata fits that model. You have scope to hunt for models in which every coefficient is significant at conventional levels and then -predict- with those. Stata lets you do that, but it remains agnostic about your prejudices about how to work with data. My own view is that insisting that every coefficient be significant is a dogma that admits many exceptions. For example, 1. Often the search for such a model involves a trawl through many possible models and makes the usual interpretation of significance level problematic. Is a project a fishing expedition or does it have a clear focus on scientific research questions? 2. Not every researcher feels compelled to use thresholds such as .05 or .01. My attitude to 0.049 is not distinguishable from my attitude to 0.051. 3. Often there are scientific and/or statistical grounds for including bundles of predictors, even if some or all don't qualify individually as significant. A perhaps esoteric example is that a sine and a cosine term usually belong together, even if one is not significant. A more common example, for many readers of this list, is that a bunch of indicators usually belong together, ditto. 4. Often much of the point of a project is to use the same model, meaning here specifically the same predictors, in different circumstances. Finding out how far results are similar or different is more straightforward than if researchers just fit a ragbag of different models and insist on everything being significant. 5. Significance is usually overemphasised in any case. Often the P-value is the dodgiest part of the results, especially if there is doubt about how far the underlying assumptions are satisfied, as there is usually is. I imagine that any others whose prejudices overlap with mine could easily extend my list. Nick n.j.cox@durham.ac.uk Mike Kim I have a question about prediction. Linear predictions in Stata (probably in all software) after any regression seem to use all estimated coefficients regardless their statistical significance. How can I understand that we use insignificant coefficients in forecasting? sysuse auto, clear reg mpg weight length // length is not significant predict mpgh, xb The predicted values use all coefficients including the coefficient of 'length' even if it is not significant. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Insignificant coefficient in prediction***From:*"Mike Kim" <kalisperos@gmail.com>

**st: RE: Insignificant coefficient in prediction***From:*Nick Cox <n.j.cox@durham.ac.uk>

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