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.psych@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

	[[alternative HTML version deleted]]

