
(a)  ${R}^{2}$ represents the proportion of variation in the dependent variable that is explained by the independent variables.
The ${R}^{2}$values can be examined to find a model with a high ${R}^{2}$value but with small number of independent variables. 

(b)  ${C}_{p}$ statistic.
A well fitting model will have ${C}_{p}\simeq p$. ${C}_{p}$ is often plotted against $p$ to see which models are closest to the ${C}_{p}=p$ line. 
On entry,  ${\mathbf{NMOD}}<1$, 
or  ${\mathbf{SIGSQ}}\le 0.0$, 
or  ${\mathbf{TSS}}\le 0.0$. 
or  ${\mathbf{MEAN}}\ne \text{'M'}$ or $\text{'Z'}$. 