We wanted to assess the impact of independent variables i.e. race, age, gender, smoking status, and PT stage over the dependent variable i.e. overall survival. There were 34 (31.2%) patients who died from disease. We used multiple linear regression to find out the independent variables which are putting impact on dependent variable and concluded that the independent variables [Age (p=0.9897), Race (p=0.6096), Gender (p=0.1350), Smoking Status (p=0.0719), and PT Stage (p=0.5023)] are not putting the impact on dependent variable. Then, by using stepwise regression we observed that all the independent variables got excluded from the model except smoking status with p-value as 0.0314. Hence smoking status is the variable which is putting impact in the model to predict overall survival duration. Introduction: There are several factors on the basis of which a particular disease increases or decreases. For example if a person is a smoker then he is more likely to have lung cancer than the non-smoker people. “Positive smoking history and male gender were independent risk factors for bladder tumor recurrence after radical nephroureterectomy. A larger number of cigarettes smoked may increase the incidence of bladder tumor recurrence in patients with upper tract urothelial cancer” (Hagiwara M et. all, 2013)1. In the same way, we want to see how the different factors are putting impact on bladder cancer. There are various studies which are already being performed to assess the impact of age and gender on bladder cancer. “It is three times more common in men than in women, and 90% of the bladder tumors are transitional cell carcinoma transitional cell carcinoma (TCC). Age, gender, and racial factors all affect the survival and prognosis of patients with bladder cancer” (Anil Mandhani et. all, 2009)2. Our interest is to see how the factors such as race, age, gender, smoking status, and PT stage are putting impact on the survival rate amongst those people who are diagnosed with bladder cancer. We have managed to get the data on the basis of which we will draw the inference for our research question. The data will be analyzed using R statistical computing and graphics. Data: We have collected data for ”OS (Overall Survival) Months”, “Age”, “Race”, “Gender (sex)”, “Smoking Status”, “PT Stage” and “Death from Disease” for 109 patients