The model looked at whether species (dog vs cat), stray intake status, and zip code predict whether an animal arrives already sterilized. Species and stray status appear to influence sterilization outcomes. The assumption checks suggest that some linear model assumptions are not perfectly met, especially normality and equal spread of residuals, which is expected because sterilization is a binary variable. To improve the model, a log transformation was applied to the zip code variable. A logistic regression would be a more appropriate method for this type of outcome in a real-world analysis.

The model looked at how an animal’s species (dog or cat) relates to whether it was already sterilized when it arrived at the shelter. The results show that species makes a difference — cats are more likely than dogs to come in already sterilized. The model explains a fair amount of the difference in sterilization status based on species alone.

This compares the relationship between sterilization and stray status. Since both are binary variables, Spearman’s method is most appropriate.

The variable Sterilization Status at Intake indicates whether an animal was sterilized when it arrived at the shelter. It’s a binary variable where 1 represents sterilized animals and 0 represents intact (unsterilized) animals. This helps assess patterns in pet sterilization across different parts of the city.

Understanding Sterilization Status in San Antonio Shelter Animals

Districts

Update of homework 2 to include codes.