anova multivariable linear regression analysis.
To perform the analysis:
- Make sure all participants have complete data on all analysis variables (BMI, Glucose, Angina, Stroke, CVD, and Hypertension).
- Use a statistical software package (such as R or SPSS) to conduct a multivariable linear regression analysis with BMI as the dependent variable and Glucose, Angina, Stroke, CVD, Hypertension, and sex as independent variables.
- Examine the coefficients for each independent variable to determine how each characteristic is related to BMI.
- Compare the crude and multivariable effects to see if there are any differences. If there are, consider what variables may be contributing to these differences.
If the null hypothesis (H0) is rejected, this would suggest that there is a relationship between BMI and the patient characteristics of Glucose, Angina, Stroke, CVD, and Hypertension in the Framingham Heart Study. If the null hypothesis is not rejected, this would suggest that there is no relationship between these variables.
It is important to note that the specific results of the analysis will depend on the dataset and variables being used, as well as the statistical software package and analysis methods used.