
THANKS so very much for your help (previous and future!). I have a two
follow-up questions.

1) You say that dispersion = 1 by definition ....dispersion changes from 1
to 13.5 when I go from binomial to quasibinomial....does this suggest that
I should use the binomial? i.e., is the dispersion factor more important
that the

2) Is there a cutoff for too much overdispersion - mine seems to be
huge......Residual deviance: 1580.1  on 123  degrees of freedom
I do have some outliers - but they are legitimate (i.e., not typos).....

I included my data below....if it helps


 summary(glm.D93)$dispersion ## 1 (by definition)

Call:
glm(formula = y ~ Pred1, family = "binomial")

Deviance Residuals:
   Min      1Q  Median      3Q     Max
-9.940  -2.778  -0.710   2.130  10.479

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)  1.63942    0.07205  22.753  < 2e-16 ***
Pred1F      -0.65228    0.11781  -5.537 3.08e-08 ***
Pred1O      -3.03239    0.12782 -23.724  < 2e-16 ***
Pred1SN     -3.60714    0.11057 -32.623  < 2e-16 ***
Pred1W      -1.22131    0.10734 -11.378  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 3506.7  on 127  degrees of freedom
Residual deviance: 1580.1  on 123  degrees of freedom
  (1 observation deleted due to missingness)
AIC: 1863.1

Number of Fisher Scoring iterations: 5

> glm1<-glm(y~Pred1,"quasibinomial")
> summary(glm1)

Call:
glm(formula = y ~ Pred1, family = "quasibinomial")

Deviance Residuals:
   Min      1Q  Median      3Q     Max
-9.940  -2.778  -0.710   2.130  10.479

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)   1.6394     0.2646   6.197 7.96e-09 ***
Pred1F       -0.6523     0.4326  -1.508  0.13415
Pred1O       -3.0324     0.4693  -6.461 2.19e-09 ***
Pred1SN      -3.6071     0.4060  -8.885 6.51e-15 ***
Pred1W       -1.2213     0.3941  -3.099  0.00241 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasibinomial family taken to be 13.48239)

    Null deviance: 3506.7  on 127  degrees of freedom
Residual deviance: 1580.1  on 123  degrees of freedom
  (1 observation deleted due to missingness)
AIC: NA

Number of Fisher Scoring iterations: 5


DATA


Pred1 PercentSurvival
O     0.181818182
O     0
O     0
O     0
O     0
F     0.766666667
F     0.967741935
F     0.8
F     0.966666667
F     0.833333333
F     0.775
F     0.641025641
F     0.272727273
F     0.606060606
F     0.621621622
F     0.574468085
F     0.918918919
F     0.854166667
F     0.684210526
A     0.438596491
A     0.8
A     0.125
A     0.936507937
A     0.911764706
A     0.75
A     0
A     0.64
A     0.740740741
A     0.703703704
A     0.962962963
A     0.911111111
A     0.97260274
A     0.842105263
A     0.795454545
A     0.970588235
A     0.755555556
A     0.947368421
A     1
A     0.947368421
A     0.785714286
A     0.782178218
A     1
A     0.6
A     0.875
A     0.625
A     0.666666667
A     1
A     1
A     0.611111111
A     0.916666667
A     0.625
A     0.97826087
A     0.975
A     0.933333333
A     1
A     1
A     0.930232558
A     0.810810811
O     0
O     0
O     1
O     0
SN    0
SN    0
SN    0.696969697
SN    0
SN    0.533333333
SN    0
SN    0.027777778
SN    0.6
SN    0.052631579
SN    0
SN    0
SN    0
SN    0.619047619
SN    0
SN    0
SN    0
SN    0.08
SN    0
SN    0.090909091
SN    0
SN    0.5
SN    0
SN    0.78125
SN    0
SN    0
SN    0
SN    0
SN    0.542857143
SN    0
SN    0
SN    0
SN    0
SN    0.4
SN    0
SN    0
SN    0.433333333
O     0.655172414
O     0.238095238
O     0
O     0.409090909
O     0
O     0
O     0.090909091
O     0.310344828
O     0
O     0
O     0
O     0.783783784
W     0.928571429
W     0
W     0.651162791
W     0.3125

W     0.871794872
W     0.511627907
W     0.566666667
W     0.756756757
W     0
W     0.666666667
W     0.55
W     0.826086957
W     0.8
W     0.682926829
W     0.586206897
W     1
W     0.75
W     0.5625

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