Table 2

Results of the hierarchical multiple regression analysis

Predictor variables	Estimate	SE	β^*	t Value	p Value
Model 1
Constant	0.31	0.003	–	114.28	<0.0001
Log proportion of Hispanic	0.91	0.01	0.29	68.52	<0.0001
Log proportion of black	0.43	0.01	0.17	41.74	<0.0001
Log proportion of families living in poverty	0.83	0.02	0.16	34.29	<0.0001
Log proportion of women older than 25 years without a high school diploma	0.34	0.02	0.09	17.33	<0.0001
Log average household size	−0.57	0.01	−0.18	−54.02	<0.0001
Urban (1=urban, 0=rural)	0.32	0.003	0.33	93.90	<0.0001
Model 2
Constant	0.26	0.003	–	81.38	<0.0001
Log proportion of Hispanic	0.11	0.03	0.03	4.12	<0.0001
Log proportion of black	0.10	0.02	0.04	4.13	<0.0001
Log proportion of families living in poverty	0.75	0.05	0.15	15.11	<0.0001
Log proportion of women older than 25 years without a high school diploma	0.05	0.04	0.01	1.33	0.183
Log average household size	−0.26	0.02	−0.08	−12.53	<0.0001
Urban (1=urban, 0=rural)	0.36	0.004	0.37	97.97	<0.0001
Urban by log proportion of Hispanic	1.01	0.03	0.29	33.16	<0.0001
Urban by log proportion of black	0.43	0.03	0.15	16.65	<0.0001
Urban by log proportion of families living in poverty	0.03	0.06	0.005	0.50	0.617
Urban by log proportion of women older than 25 years without a high school diploma	0.33	0.04	0.08	7.73	<0.0001
Urban by log average household size	−0.44	0.02	−0.13	−18.35	<0.0001

R²=0.39 for Step 1, ∆R²=0.02 (p<0.0001).
The column header Estimate is the unstandardised parameter estimate. For all predictor variables except the binary urban/non-urban variable, the estimate represents the per cent change in the tobacco outlet density per 1000 population for a 1% change in the predictor variable since both the predictor and the outcome variables are log transformed. In the case of the binary urban/non-urban predictor variable, which is not log transformed, the estimate is the (100 × estimate) per cent change in the outcome variable for a unit change in the predictor variable. For the interaction terms, the interpretation is the additional percentage increase for urban census tracts versus rural census tracts. Thus, for instance, a 1% increase in the proportion of Hispanics in non-urban census tracts is associated with a 0.11% increase in the tobacco outlet density per 1000 population. For urban communities, there is an additional 1.01% change totalling to a 1.12% increase for a 1% increase in the proportion of Hispanics compared with a 0.11% increase in non-urban census tracts.
↵* Standardized parameter estimate.