Table 4.

Forward stepwise models predicting annual pet cases (square root transformed)^*

Model	coef	P var	P model	R2	AIC	df	Link	OV test	Het test
Model 1: Regress pet (square root transformed) on
			0.0001	0.436	95.13	2	–	–	0.13
Surveillance 1991	2.2	0.000
Model 2: Regress pet (square root transformed) on
			< 0.0001	0.520	87.35	3	0.66	0.79	0.53
Surveillance 1991	2.13	0.000
1 yr lag precip	0.207	0.013
Model 3: Regress pet (square root transformed) on
			< 0.0001	0.658	72.71	4	0.35	0.75	0.94
Surveillance 1991	2.17	0.000
1 yr lag precip	0.29	0.000
3 yr lag > 27°C	0.11	0.002
Model 4: Regress pet (square root transformed) on
			< 0.0001	0.749	63.33	5	0.49	0.32	0.14
Surveillance 1991	1.89	0.000
1 yr lag precip	0.36	0.000
3 yr lag > 27°C	0.09	0.006
4 yr lag winter temperature maxium	0.27	0.009

^*The final model included a dichotomized surveillance variable (0 = pre-1991), 1 year lagged annual average precipitation (precip), 3 year lagged total degrees over 27°C, and 4 year lagged winter temperature maximum. Models were compared by Akaike information criterion (AIC) and R² and tested for validity using a link test, omitted variable test (OV test), and for heteroskedasticity (Het test). Winter spans the months of January through April.