This paper explores different ways to generate random numbers and estimate integrals using computer simulation in R. We start by sampling from the Cauchy distribution and then use it as a tool to generate normally distributed numbers. Next, we work with a special version of the Gamma distribution that is restricted to values above 4, testing two different sampling strategies to see which one wastes fewer attempts. Finally, we calculate a specific integral using five different simulation approaches and compare how accurate and efficient each one is.

Computational Statistics - Problems on Optimizations and Random Number Generation.

This is the assignment related to verifying the robustness of one sample t-Test, Verifying the correlation using Permutation test, Bootstrap Analysis on the `scor` data set.