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Confounders versus including them in a regression model

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Reference no: EM131190951

Interpret the two models that appear below, and address the following additional questions as they pertain to each.

BMI (1 unit) = 1.3 + 2.4 (diabetes) + 2.3 (family history diabetes) + 1.7 (gender) + 1.4 (age) + 1.7 (race) + 2.6 (income) + 3.4 (height), p<0.05

Allergies = 4.5 + 3.8 (Family History Allergies) + 2.1 (gender) + 1.4 (age) + 0.8 (race) + 1.5 (weight), p<0.05

What about confounding? Which of the variables are potential confounders?

Compare and contrast matching on potential confounders versus including them in a regression model.

Reference no: EM131190951

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