Suppose researchers are interested in the relationship between systolic blood pressure and periodontal disease in a population of low-income families. In order to address the relationship most efficiently, the researchers have decided to model age as a potential confounding variable. During the analysis phase of their investigation, the researchers found it most appropriate to model age as a dummy variable. The variable was quantified in quartiles, and grouped in the following manner.
Category1) Age<25
Category2) Age (25, 35)
Category3) Age (36, 45)
Category4) Age >45
The reference category to which each of the others will be compared is : Age>45
Complete the following table and organize these categories into a set of dummy variables. Describe what a 1 and 0 indicate for each dummy variable.
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X1 |
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Age<25 |
X1: 1 if |
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Suppose researchers are interested in the relationship between systolic blood pressure and periodontal disease in a...
Suppose researchers are interested in the relationship between daily intake of caffeine and seizures. There are four possible values for this variable: Category1) 0-99 mg Category2) 100-299 mg Category3) 300-499 mg Category4) ≥ 500 mg The reference category to which each of the others will be compared is the value 0-99 Organize these categories into a set of dummy variables. Describe what a 1 and 0 indicate for each dummy variable.
Consider the following model constructed by researchers investigating the relationship between blood pressure and BMI, controlling for age: BMI=α+ β1*(Systolic blood pressure)+β2*(Age) A) Describe the interpretation of each of the following: α, β1, β2 B) How many dimensions are being observed in the response surface depicted by this model
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1. When studying the relationship between ankle-brachial blood pressure index and peripheral vascular disease the researchers have a significant R square. They then add a second independent variable of a pain scale. The R square in the model that includes both independent variables is significant but the R square change is not. You know this means: A. The second variable added may not be significant and should be examined further B. Having both variables in the model explains significantly more...