Churn

This project demonstrates the application of statistical simulation in R to model real-world healthcare scenarios using both discrete and continuous probability distributions. The objective of this analysis is to understand how random variables can be used to represent real phenomena such as the number of patients arriving at a hospital and the recovery time after medical treatment. In the first part of the analysis, a Poisson distribution is used to simulate the number of heart disease cases arriving at a hospital each day. Based on historical data, the average number of heart disease patients is assumed to be 30 cases per day. A simulation is performed for 60 days to observe the variability and distribution of daily cases. In addition, the study presents a simple visualization of patient outcomes, where the proportion of recovered patients and deceased patients is displayed using a bar chart. The second part focuses on a continuous probability distribution, specifically the normal distribution, to model patient recovery time after minor surgery. The average recovery time is assumed to be 14 days with a standard deviation of 3 days. Using this model, the analysis calculates several probabilities related to recovery time, such as the probability of a patient recovering in less than 10 days and the probability of recovery occurring between 12 and 18 days. A simulation of 100 patients is also conducted to visualize the distribution of recovery times. Finally, an additional simulation is conducted to model the number of patients arriving at an emergency department each day. Assuming that patient arrivals follow a Poisson distribution with an average of 25 patients per day, the simulation generates data for 50 days. The analysis explores the distribution of daily arrivals, calculates the average number of patients from the simulated data, and estimates the probability of having 30 or fewer patients in a single day. Overall, this project illustrates how probability distributions and simulation techniques in R can be used to better understand variability in healthcare-related processes and to visualize patterns in simulated dat

Chi-Quadrat-Varianztest