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From |
Kieran McCaul <kieran.mccaul@uwa.edu.au> |

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

Subject |
st: RE: Interpretation of coefficients for predictor with linear and quadratic terms in negative binomial regression |

Date |
Mon, 21 Oct 2013 18:47:57 +0800 |

... It's hard to comment on this without knowing more about the data you have. There are two things I don't understand. 1. You are using the number of children who have attended a child care centre in a year as the offset. How is this different from occupancy? 2. How long are there periods of extended leave? Are they long enough to create a short-term vacancy which the child care centre would fill? In other words, would an exit cause an increase in the number of children attending a child care centre in a year? -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Rachael Wills Sent: Monday, 21 October 2013 12:10 PM To: statalist@hsphsun2.harvard.edu Subject: st: Interpretation of coefficients for predictor with linear and quadratic terms in negative binomial regression Dear all, I am trying to quantify the effect of the occupancy (%) of child care centres on the tendency of children to take extended departures from the centre. I am using -nbreg- in Stata MP 12.1 to run a negative binomial regression where the outcome is the number of children at a centre taking an extended departure during the year (variable is called 'exits') with an offset term containing the number of children attending the centre at all during the year (variable is called 'children'). A scatterplot of 'exits' as a percentage of 'children' against occupancy (%) suggests that a term in occupancy squared may also be necessary, and indeed both linear and quadratic terms are significant at the 95% level in the model: gen occ2 = occ^2 nbreg exits occ occ2, exposure(children) My question then, is how I can interpret the dual coefficients for the occupancy terms. Is it best to use the coefficients, or can a simpler interpretation be made using the -irr- option? I would like to be able to provide a statement such as 'For every 1% increase in occupancy there is a X decrease in the exit rate'. However, I'm not even sure if such a simple statement is possible when there are both linear and quadratic terms involved. Any advice would be much appreciated. Thanks Rachael Wills * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/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/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Interpretation of coefficients for predictor with linear and quadratic terms in negative binomial regression***From:*Rachael Wills <rwills@goodstart.org.au>

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