Diabetes Model Outcomes Results

Biostatistics
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Biostatistics
Diabetes Model Outcomes Results
Diabetes (1 unit) = 1.3 + 2.4 (BMI) + 2.3 (family history diabetes) + 1.7 (gender) + 1.4 (age) + 1.7 (race) + 2.6 (income) + 3.4 (height), p<0.05
The diabetes data result framework provides a dependent body mass index (BMI) variable associated with different predictor variables in research. Such variables include a family history of diabetes, income level, height, age, gender, and diabetes (Cheung, 2015). Physicians use the result of the summary model to examine and determine how independent variables impact the body mass index. For example, the above model shows that the coefficient of diabetes is 2.4. This signifies that an increase in diabetes by a single unit results from a 2.4 increase in the units of the body mass index. Hence, the projected results of the BMI are often attributed to the outcomes of diabetes tests (Cruyff et al., 2016). This implies that independent or predictor variables generate a regression with dependent variables. The regression examination depicts a statistically substantial variation for the BMI outcome depending on the expected result of the p-value, P < 0.05.
The implication of confound is depicted as a variable that impacts the results of the independent and dependent variables. For instance, the confounding variables in the data model results entail income, race, age, gender, and heights. These variables are often used alongside matching variables like gender, race, and age. The matching variables are particularly critical to research analysis since they give an alternative and facilitate elaborate explanations to research findings (Cheung, 2015). In effect, this helps to enhance the model relationship between the independent and dependent variables (Cruyff et al., 2016). For instance, it posits that tall people are more susceptible to diabetes since they have a relatively higher coefficient than other determinant variables.
Allergies Outcomes Model Results
Allergies = 4.5 + 3.8 (Family History Allergies) + 2.1 (gender) + 1.4 (age) + 0.8 (race) + 1.5 (weight), p<0.05
The allergies outcome results models have outcomes for allergies that is a dependent variable to other determinant variables. Hence, the research analysis is d...