I’ve got two main groups, which I am comparing, a) rats and b) rabbits. Each group consists of two subgroups – breed 1 and breed 2.
Using linear regression, I would need to find out, does a model taking into account the subgroups account for more variance than a model with only the main groups.
So I have created two models: \begin{align} Y &= a_1+b1_1(\text{rab})+e_1 \tag{1} \\ Y &= a_2+b1_2(\text{rat2})+b2_2(\text{rab1})+b3_2(\text{rab2})+e_2\ \tag{2} \end{align} Where all variables are dummy variables:
- $\text{rab}\ \ = 1$ for a rabbit and zero otherwise (e.g., rats)
- $\text{rab1} = 1$ for rabbit breed 1
- $\text{rab2} = 1$ for rabbit breed 2
- $\text{rat2}\, = 1$ for rat breed 2
In order to compare the models statistically, I would need to know, whether my models can be considered nested or not.
If the models are considered nested, I would also need advice in how to compare them statistically - preferably with SPSS. With non-nested models I can handle the rest.