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Table 2 Linear regressions that predict the three chronic diseases from item weight and nutrient diversity (plus control variables such as income, gender, age, education level)

From: Large-scale and high-resolution analysis of food purchases and health outcomes

Feature Coefficient Std. error p-value
Hypertension
α (intercept) 0.5582 0.064 <0.001
Calorie consumption 0.30228 0.070 <0.001
Nutrient diversity −0.5182 0.069 <0.001
Income 0.0615 0.041 0.131
%Females −0.2210 0.038 <0.001
Average age 0.1627 0.037 <0.001
Education −0.2309 0.041 <0.001
Durbin–Watson stat. = 2.033 Adj \(R^{2} = 0.377\)
Cholesterol
α (intercept) 0.5465 0.059 <0.001
Calorie consumption 0.1395 0.064 0.03
Nutrient diversity −0.4943 0.064 <0.001
Income 0.0017 0.037 0.96
%Females −0.2364 0.035 <0.001
Average age 0.11790 0.034 0.001
Education −0.1785 0.037 <0.001
Durbin–Watson stat. = 1.981 Adj \(R^{2} = 0.344\)
Diabetes
α (intercept) 0.7582 0.038 <0.001
Calorie concentration 0.1301 0.028 <0.001
Nutrient diversity −0.6353 0.043 <0.001
Income −0.0790 0.034 0.019
%Females −0.3693 0.031 <0.001
Average age −0.0606 0.030 0.042
Education 0.1047 0.032 0.001
Durbin–Watson stat. = 1.964 Adj \(R^{2} = 0.585\)
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