Dear all

I have data related to cell count across time in 2 different types of
cells. I have transformed the count data using a log
and I want to test the H0: B cell_ttype1=Bcell_type2  across time

for that I am fitting the following model

fit_all<-lm(data$count~data$cell_type+data$time+data$cell_type*data$time)

the output is

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)
(Intercept)                 1.0450021  0.0286824  36.434   <2e-16 ***
data$cell_typeOV           -0.0456669  0.0405631  -1.126    0.271
data$time                   0.0115620  0.0004815  24.015   <2e-16 ***
data$cell_typeOV:data$time -0.0009764  0.0006809  -1.434    0.164
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.06318 on 26 degrees of freedom
Multiple R-squared:  0.9764,    Adjusted R-squared:  0.9737
F-statistic: 358.8 on 3 and 26 DF,  p-value: < 2.2e-16


inspite the fact that the p-value of he interaction is >0.05 may I still
conclude that B cell_ttype1 is different from Bcell_type2 because the
p-value of the fit is lower<0.05?

Thanks in advance for your help.

With kind regards,

Andreia

-- 
---------------------------------------------------------------------------------------------
Andreia J. Amaral, PhD
BioFIG - Center for Biodiversity, Functional and Integrative Genomics
Instituto de Medicina Molecular
University of Lisbon
Tel: +352 217500000 (ext. office: 28253)
email:andreiaamaral@fm.ul.pt ; andreiaamaral@fc.ul.pt

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