Maybe I should simplify the problem with the following smaller table. And I
just want to ask whether
there is any significant difference in the proportion of "Good_Sample"
produced by factories located in "City_A" and "City_B".
Factory_ID Factory_Location Total_Sample Good_Sample
----------------------------------------------------------------------------------------
1 City_A 100 90
2 City_A 120
55
3 City_A 80
40
4 City_A 75
50
5 City_B 150
80
6 City_B 120 55
7 City_B 125
40
8 City_B 100 60
9 City_B 70 45
10 City_B 85 65
----------------------------------------------------------------------------------------
On Mon, Mar 22, 2010 at 2:56 PM, Joshua Wiley <jwiley.ps...@gmail.com>wrote:
> I am not completely sure what your regression model looks like (what
> your outcome and predictors are). It seems like you have different
> levels of data (samples nested in factories nested in cities). What
> question do you really want to answer? You might consider looking
> into multi-level analyses. Douglas Bates has an excellent package
> "lme4" that works with nested models. Particularly check out ?glmer
> for the multi-level equivalent of glm(). I don't know if that really
> gets to your question of dealing with individual factory, but it is at
> least designed to handle different levels. I only have a rudimentary
> knowledge of multi-level models or logistic regression so I cannot
> offer much advice.
>
> Best of luck,
>
> Joshua
> \
>
--
Xiang Gao, Ph.D.
Department of Biology
University of North Texas
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