Curcumin Rich Diet Lowers The Incidence Of Breast Cancer Essay

Curcumin Rich Diet Lowers The Incidence Of Breast Cancer - Essay Example Curcumin, (diferuloylmethane), the yellow pigment extracted from the rhizoma of Curcuma longa, has been expected to posses therapeutic or preventive value for several cancers because of its antioxidative, anti-inflammatory, and anticancerous effects (Maheshwari et al., 2006) Cancer incidence rate comparing the breast and prostate cancer incidence in US and India is presented in the Table 1. Date from GLOBOCAN 2000 is a unique software program which provides access to information on the incidence and prevalence of, and mortality from 26 major cancers for all the countries in the world in 2000. Thus, the cancer incidence, in people from US, is for people who don’t consume curcumin, whereas the cancer incidence, in people from India, is for people who consume curcumin.... 2. P (eat curcumin and don't get breast cancer) = P (eat curcumin) P (don't get breast cancer) = 1000000/2000000 999921/2000000 = 0.5 0.4999605 = 0.24998025. The number of people who eat curcumin and don't get breast cancer is P (don't eat curcumin and don't get breast cancer) total number of people = 0.24998025 2000000 = 499960.5 3. P (don't eat curcumin and get breast cancer) = P (don't eat curcumin) P (get breast cancer) = 1000000/2000000 660/2000000 = 0.5 0.00033 = 0.000165. The number of people who don't eat curcumin and get breast cancer is P (don't eat curcumin and get breast cancer) total number of people = 0.000165 2000000 = 330 4. P (don't eat curcumin and don't get breast cancer) = P (don't eat curcumin) P (don't get breast cancer) = 1000000/2000000 999340/2000000 = 0.5 0.49967 = 0.249835. The number of people who eat curcumin and don't get breast cancer is P(don't eat curcumin and don't get breast cancer) total number of people = 0.249835 2000000 = 499670 We place the values obtained in this table: (this is our null distribution) Breast cancer No Breast cancer Total Curcumin Yes (India) 39.5 499960.5 500000 No (USA) 330 499670 500000 Total 369.5 999630.5 1000000 Using the formula, where Oi is observed numbers or frequency and Ei is what we expect if there is no dependence between the 2 variables. we get, 2 = (79-39.5)2/39.5 + (999921-499960.5)2 / 499960.5 + (660-330)2 / 330 + (999340-499670)2 / 499670 = 1000000 The larger the value of 2 , the worse the fit To see whether this number is significant we calculate the p-value. p-value = P(2 > a specific value | model is correct) In this case, the specific value