anova multivariable linear regression evaluation.

To carry out the evaluation:

- Be sure all members have full knowledge on all evaluation variables (BMI, Glucose, Angina, Stroke, CVD, and Hypertension).
- Use a statistical software program package deal (comparable to R or SPSS) to conduct a multivariable linear regression evaluation with BMI because the dependent variable and Glucose, Angina, Stroke, CVD, Hypertension, and intercourse as impartial variables.
- Look at the coefficients for every impartial variable to find out how every attribute is expounded to BMI.
- Examine the crude and multivariable results to see if there are any variations. If there are, contemplate what variables could also be contributing to those variations.

If the null speculation (H0) is rejected, this is able to recommend that there’s a relationship between BMI and the affected person traits of Glucose, Angina, Stroke, CVD, and Hypertension within the Framingham Coronary heart Examine. If the null speculation shouldn’t be rejected, this is able to recommend that there isn’t a relationship between these variables.

You will need to observe that the particular outcomes of the evaluation will rely on the dataset and variables getting used, in addition to the statistical software program package deal and evaluation strategies used.