> poi1 <- glm(formula = wwcount ~ 1, family=poisson(link=log), data=ww21)
> summary(poi1)

Call:
glm(formula = wwcount ~ 1, family = poisson(link = log), data = ww21)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6733  -1.6733  -0.3564   0.4761   4.2771  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  0.33647    0.04688   7.177 7.11e-13 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 565.52  on 324  degrees of freedom
Residual deviance: 565.52  on 324  degrees of freedom
AIC: 1097.8

Number of Fisher Scoring iterations: 5

> 
> poi2 <- glm(formula = wwcount ~ 1, family=poisson(link=log), data=ww22)
> summary(poi2)

Call:
glm(formula = wwcount ~ 1, family = poisson(link = log), data = ww22)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.0046  -1.0046  -1.0046   0.6141   3.0937  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.68396    0.07809  -8.759   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 356.06  on 324  degrees of freedom
Residual deviance: 356.06  on 324  degrees of freedom
AIC: 622.97

Number of Fisher Scoring iterations: 6

> 
> poi3 <- glm(formula = wwcount ~ 1, family=poisson(link=log), data=ww23)
> summary(poi3)

Call:
glm(formula = wwcount ~ 1, family = poisson(link = log), data = ww23)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.3056  -1.3056   0.1557   0.1557   3.0655  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)   
(Intercept) -0.15981    0.06008   -2.66  0.00782 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 452.55  on 324  degrees of freedom
Residual deviance: 452.55  on 324  degrees of freedom
AIC: 840.27

Number of Fisher Scoring iterations: 5

> 
> poi4 <- glm(formula = wwcount ~ 1, family=poisson(link=log), data=ww24)
> summary(poi4)

Call:
glm(formula = wwcount ~ 1, family = poisson(link = log), data = ww24)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.9081  -0.9081  -0.9081   0.7724   3.3171  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.88599    0.08639  -10.26   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 346.31  on 324  degrees of freedom
Residual deviance: 346.31  on 324  degrees of freedom
AIC: 564.41

Number of Fisher Scoring iterations: 6

> 
> library(MASS)
> 
> nb1 <- glm.nb(formula = wwcount ~ 1, data=ww21)
> summary(nb1)

Call:
glm.nb(formula = wwcount ~ 1, data = ww21, init.theta = 1.911309431, 
    link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4494  -1.4494  -0.2769   0.3522   2.7333  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  0.33647    0.06171   5.453 4.96e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for Negative Binomial(1.9113) family taken to be 1)

    Null deviance: 360.3  on 324  degrees of freedom
Residual deviance: 360.3  on 324  degrees of freedom
AIC: 1052

Number of Fisher Scoring iterations: 1


              Theta:  1.911 
          Std. Err.:  0.413 

 2 x log-likelihood:  -1048.005 
> 
> nb2 <- glm.nb(formula = wwcount ~ 1,data=ww22)
> summary(nb2)

Call:
glm.nb(formula = wwcount ~ 1, data = ww22, init.theta = 3.022068476, 
    link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.9661  -0.9661  -0.9661   0.5573   2.6248  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.68396    0.08435  -8.108 5.14e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for Negative Binomial(3.0221) family taken to be 1)

    Null deviance: 306.94  on 324  degrees of freedom
Residual deviance: 306.94  on 324  degrees of freedom
AIC: 621.52

Number of Fisher Scoring iterations: 1


              Theta:  3.02 
          Std. Err.:  1.92 

 2 x log-likelihood:  -617.515 
> 
> nb3 <- glm.nb(formula = wwcount ~ 1, data=ww23)
> summary(nb3)

Call:
glm.nb(formula = wwcount ~ 1, data = ww23, init.theta = 2.15767548, 
    link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.1986  -1.1986   0.1308   0.1308   2.3004  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)  
(Intercept) -0.15981    0.07097  -2.252   0.0243 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for Negative Binomial(2.1577) family taken to be 1)

    Null deviance: 341.14  on 324  degrees of freedom
Residual deviance: 341.14  on 324  degrees of freedom
AIC: 826.72

Number of Fisher Scoring iterations: 1


              Theta:  2.158 
          Std. Err.:  0.719 

 2 x log-likelihood:  -822.718 
> 
> nb4 <- glm.nb(formula = wwcount ~ 1, data=ww24)
> summary(nb4)

Call:
glm.nb(formula = wwcount ~ 1, data = ww24, init.theta = 1.423407896, 
    link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.8510  -0.8510  -0.8510   0.6525   2.5354  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -0.8860     0.0981  -9.031   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for Negative Binomial(1.4234) family taken to be 1)

    Null deviance: 268.43  on 324  degrees of freedom
Residual deviance: 268.43  on 324  degrees of freedom
AIC: 557.59

Number of Fisher Scoring iterations: 1


              Theta:  1.423 
          Std. Err.:  0.632 

 2 x log-likelihood:  -553.591 
>