This document is the final report of the Peer Assessment project from Coursera’s course Practical Machine Learning, as part of the Specialization in Data Science. It was built up in RStudio, using its knitr functions, meant to be published in html format. This analysis is meant to be the basis for the course quiz and a prediction assignment write up. The main goal of the project is to predict the manner in which 6 participants performed some exercise as described below. This is the classe variable in the training set. The machine learning algorithm described here is applied to the 20 test cases available in the test data

This project involves exploring the relationship between miles per gallon (MPG) and transmission in a data set of a collection of cars. Two questions are of interest: Is an automatic or manual transmission better for MPG? and what is the quantifiable MPG difference between automatic and manual transmissions? It was found that the weight of the car (`wt`) is a confounding variable in the relationship between miles per gallon (`mpg`) and transmission (`am`). It was also found that manual transmission cars have, on an average, 1.55 times more mpg than automatic transmission cars.

This report aims to analyze the ToothGrowth data in the R datasets package. The aim is compare tooth growth by supplement delivery method and by dose using hypothesis tests and confidence intervals to arrive at a conclusion.

In this project I investigated the exponential distribution in R and compared it with the Central Limit Theorem. I investigated the distribution of averages of 40 exponentials and carried out a thousand simulations.

This is the second course project for Reproducible Research course on Coursera. In this project, the effects of severe weather events across the United States on the population health and the economy were studied. Data from the U.S. National Oceanic and Atmospheric Administration was analyzed using RStudio.