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I have fitted a Poisson regression to my claim frequency.

I have obtained the following result:

              Estimate   Std. Error  z value   Pr(>|z|) 
(Intercept)  -19.95861   1139.33678   -0.018     0.9860 
make          -0.10534      0.04116   -2.559     0.0105 *
agevehO        0.05983      0.08580    0.697     0.4856
area1         20.68177   1139.33677    0.018     0.9855 
area2         20.85866   1139.33677    0.018     0.9854 
area3         20.76927   1139.33676    0.018     0.9855  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Null deviance:        657.49 on 583 degrees of freedom
Residual deviance: 150.62 on 578 degrees of freedom
AIC: 1096.

My predictors area i have four levels (area 1,2,3,4) and agveh two (old and new), however for make i have four levels (make 1,2,3,4), how come it is not showing the three levels?? i am confused on how to interprete this result? Also my deviance table showed the following :

             DF   DEVIANCERESID DF RESIDDEV PR(>CHI)
make         1        13.61    582   643.88 0.0002251 ***
ageveh       1         9.80    581   634.08 0.0017460 ** 
area         3       483.47    578   150.62 < 2.2e-16 ***

Bases on this can i conclude that make ageveh and area are statistically significant in explaining my claim frequency? Thanks

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1 Answer 1

This means that your make variable is treated as a numeric variable instead of a categorical variable. Try to change your make variable to a factor variable instead (e.g., if your dataset is stored in data frame data, then you could do data$make <- as.factor(data$make) ), then run your regression again.

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thanks so much now it works @ Patrick Coulombe –  user40494 Feb 22 at 20:34
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