In order to adequately compare each factor a two-way anova would be a good analyzing technique. A two way anova is used for finding a F-values and p-values of a data set. This can compare one group difference or several group differences. A p-value of less than 0.05 will indicate significant difference between groups. The large data set will require software analysis because we are finding the significance of each group as well as the smoker, non-smoker, and past smoker with and without the effects of diet. To understand how much these factors effect cholesterol level every group and factor must be analyzed. From this the relative risk factor of smoking as well as the added effects of diet can be …show more content…
By implementing statistical analysis and research design this study was aimed to test blood cholesterol levels in order to better understand the risk factors and long term effects of smoking on heart disease. This is an important project because of how much damage heart disease is responsible for, while this project is far from perfect it was created to produce results quickly. Other studies have compared medical files to death records to get an idea of how many deaths heart disease is responsible for but these experiments take several years and offer no treatment to the subjects. This study was designed because as a medical student, I believe data must be collected but not at the expense of the subjects. The future of medical research is bright and this disease and many others will be cured once it is better