Abstract: Biostatistics, a discipline in mathematics, is concerned with collecting, analyzing, and interpreting data from biological studies. Application of biostatistics models can be found in a myriad of areas of research. Several examples of applications of biostatistics to public health, epidemiology, basic science, and clinical research are discussed. The Framingham Heart Study (FHS) contributed to understanding the role of traditional risk factors, high blood pressure, diabetes, and smoking in coronary heart disease (CHD). Statisticians from the FHS developed mathematical models for the prediction of CHD mortality. Obesity is one of the leading factors associated with high blood pressure and diabetes. We discuss the relationship between obesity and all-cause mortality in the US population and propose a method for estimating an “ideal” weight-by-height reference. Vitamin D, a hormone associated with bone health, has been suggested as a risk factor for cardiovascular disease. However, vitamin D measurement has not been standardized, and thus reference population values are difficult to define. A vitamin D calibration approach will be presented. A piecewise statistical regression model for estimating vitamin D reference values will be introduced. The rate of type 2 diabetes has been increasing in the US due possibly to modifiable risk factors, such as obesity, unhealthy diet, and physical activity. These factors are believed to be among the main contributors to the recent increase in T2D in children. Linear mixed-effects models were applied to unveil a potential novel risk factor for T2D incidence in children.