I have two **logit models**:

 - **Model A:**

```{r}
model_logit_house <- glm( health_status ~ sex + age + weight + study_level + chronic_ill +  laboral_situation +
                           sport_frec   + GHQ_12 + income_level +
                           n_bedrooms + indust_pollution +    delinquency, # study variables
                         data = model_data, family = binomial(link = "logit"),na.action = "na.omit")
```

 - **Model B (a nested model from Model A):**

```{r}
model_logit <- glm( health_status ~ sex + age + weight + study_level + chronic_ill +  laboral_situation +
                           sport_frec   + GHQ_12 + income_level +, 
                       data = model_logit_house$model, family = binomial(link = "logit"))
```
`c(weight,GHQ_12,income_level)` are continuous variables, the rest variables are cualitatives (factor).

**I want to analyze if the housing characteristics (study variables) have influence on the health status**. All variables are significant in both models. However, I want to do an analysis of variance (ANOVA) between these two models for to be sure that model A is better than model B. So:

`anova(model_logit,model_logit_house)`
And the output is:

```
  Resid. Df Resid. Dev Df Deviance
1     16805      15439            
2     16802      15420  3   18.644
```

How do I interpret this table? Can the interpretation of this table tell me if there is an influence between the housing characteristics and the health status? If not. **How can I compaire these two models with an ANOVA in R?**