> apprent.mod1 <- glm(apprentices ~ 1, family=poisson("log"))
> summary(apprent.mod1)

Call:
glm(formula = apprentices ~ 1, family = poisson("log"))

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-5.326  -4.589  -3.612  -1.476  28.674  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  2.65196    0.04622   57.37   <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: 1350.4  on 32  degrees of freedom
Residual deviance: 1350.4  on 32  degrees of freedom
AIC: 1446.8

Number of Fisher Scoring iterations: 6

> 
> apprent.mod2 <- glm(apprentices ~ distance+logpop+urban+direction, 
+    family=poisson("log"))
> summary(apprent.mod2)

Call:
glm(formula = apprentices ~ distance + logpop + urban + direction, 
    family = poisson("log"))

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-4.7326  -1.2122  -0.2589   1.8502   6.4246  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  1.334354   0.420763   3.171  0.00152 ** 
distance    -0.032409   0.001844 -17.574  < 2e-16 ***
logpop       1.061387   0.079851  13.292  < 2e-16 ***
urban       -0.027936   0.004052  -6.894 5.44e-12 ***
direction2   0.103277   0.174946   0.590  0.55496    
direction3   0.812827   0.142741   5.694 1.24e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 1350.44  on 32  degrees of freedom
Residual deviance:  212.46  on 27  degrees of freedom
AIC: 318.8

Number of Fisher Scoring iterations: 6

> 
> anova(apprent.mod1, apprent.mod2, test="Chisq")
Analysis of Deviance Table

Model 1: apprentices ~ 1
Model 2: apprentices ~ distance + logpop + urban + direction
  Resid. Df Resid. Dev Df Deviance  Pr(>Chi)    
1        32    1350.44                          
2        27     212.46  5     1138 < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1