Approximately 1 in 3 women will develop breast cancer each year. The key to the treatment of breast cancer is early detection and intervention. Analysis of the data on breast cancer rates and demographics may be useful in predicting which groups are more likely to develop breast cancer and recommending frequent and early screenings. The study utilized data from the Breast Cancer Surveillance Consortium which publishes data on breast cancer rates with demographic data which they compiled through collaboration with clinicians across the United States. Data analysis tools available through the JMP 16 software were used to look into the different categorical demographic groups and to assess which groups were statistically more predisposed to developing breast cancer. Analysis of the distributions as well as one-way Analysis of Variance of the different categories (year, age group, race/ethnicity, age of menarche, age of firstborn, breast density, current heart rate, age of menopause, body mass index (BMI), and breast cancer history) was conducted. The major results come from the Anova and Tukey-HSD test which was used to discern statistically unique groups. The study found that having no hormone replacement therapy, pre/peri-menopausal, BMI group 1(10-24.99), Non-Hispanic White, and Scattered fibroglandular densities were all observed with having statistically greater breast cancer rates. The results suggest that an individual belonging to one or more categories (having no hormone replacement therapy, pre/peri-menopausal, BMI group 1(10-24.99), Non-Hispanic White, and Scattered fibroglandular densities) should have more frequent and earlier screenings.