Regression analysis can estimate the magnitude of the impact of a change in one variable or another (Holmes et al., 2017). If regression analysis were to be completed on BMI, an independent variable associated with that would be height and weight. The size and weight are how the BMI is calculated. Usually, as we get older, the amount of body fat increases. This could be because we typically become less active as we age. Additionally, females usually have a higher percentage of body fat than males. Other independent variables included in the analysis would be sex, age, diet, body type, and disease processes. A statistic that will show the value of BMI regression would be physical activity. Typically, the more physical activity a person gets, the lower their BMI.
The article that I found interesting that uses regression analysis to study a medical concern discussed the relationship between changes in dietary cholesterol intake and alterations in lipoprotein-cholesterol markers for cardiovascular disease risk and provided a reference for clinicians on how changes in dietary cholesterol intake affect circulating cholesterol concentrations, after accounting for intakes of fatty acids (Vincent et al. 2019). The independent variables discussed in the article were trans fatty acid intake, saturated fatty acid intake, and cholesterol intake. The dependent variable is LDL-cholesterol concentration. I would use this study to highlight the difference between correlations and causation by accepting the conclusion that dietary cholesterol change was positively associated with the change in LDL-cholesterol concentration (Vincent et el. 2019).
Holmes, A., Illowsky, B., & Dean, S. (2017). Introductory business statistics. OpenStax.
Vincent, M. J., Allen, B., Palacios, O. M., Haber, L. T., & Maki, K. C. (2019). Metaregression analysis of the effects of dietary cholesterol intake on LDL and HDL cholesterol. The American journal of clinical nutrition, 109(1), 7–16