In this exp we compare to groups, who got the VC supplement and the other who got the OJ supplement. We consider the two groups to be Independent as there's no matching of the subjects between them and considering tooth growth may be subjected to only one subject in an experiment. For calculating T-Intervals,we consider 95% confidence, variances as different and alpha value is 0.05.

This report describes the usage of Linear and Multivariate Regression to predict the MPG (Miles Per Gallon) of Automatic Vs Manual Transmission vehicles. This report was made as an assignment for the coursera Regression Models course.

This report explains the Central Limit Theorem in practice. This report was made as an assignment of the coursera Statistical Inference course.

This report shows the reader how to per form 'Webscraping',that is, extract data from a webpage and how to make a geographical plot using location coordinates in R. This report shows the extraction of the data of the top 30 Restaurants in Worcester, USA from the 'tripadvisor.in' webpage. Presents extraction of information about the number of reviews, the rank(out of 30) from "http://www.tripadvisor.in/Restaurants-g41952-Worcester_Massachusetts.html". This report shows how I 've used Webscraping to find Geological Coordinates from google maps without the use of googlemaps API and plotting the coordinates on google maps using the ggmaps package.

This application is used for webscraping the data from 'www.transfermarkt.com' of the world's best players 'Messi' and 'Ronaldo'. The function is written in R and tabularizes and plots the number of goals scored or assists made by Messi or Ronaldo. User must input the name of the player as 'cristiano-ronaldo' with id =8198 or 'lionel-messi' with id=28003 and the season whos data the user wants to see.

In this Data Analysis, I have summarized the consequences on population health and economic losses of Storms and other severe weather events across United States. This data analysis project explores the 'Storm Data Preparation'(raw data set) which tracks characteristics of major storms and weather events in the United States, including when and where they occur, also shows estimates of any fatalities, injuries, and property damage. The data set can be downloaded from "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2" in the csv format and some documentation on the data set can be availed from "https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf" in pdf. In the first part of the analysis, consequences on population health by disastrous events have been summarised. In the second part of the analysis, economic losses due to property and crops damages occured as a result of these disastrous events have been accounted.

This human activity recognition research has traditionally focused on discriminating between different activities, i.e. to predict "which" activity was performed at a specific point in time. This research is based on Human Activity Recognition(HAR) which have many potential applications for HAR, like: elderly monitoring, life log systems for monitoring energy expenditure and for supporting weight-loss programs, and digital assistants for weight lifting exercises. In this Machine Learning Experiment I've used the WLE Dataset (cite: http://groupware.les.inf.puc-rio.br/public/papers/2013.Velloso.QAR-WLE.pdf*). Six young health participants were asked to perform one set of 10 repetitions of the Unilateral Dumbbell Biceps Curl in five different fashions: exactly according to the specification (Class A), throwing the elbows to the front (Class B), lifting the dumbbell only halfway (Class C), lowering the dumbbell only halfway (Class D) and throwing the hips to the front (Class E). This report shows how I've created a Machine Learning Model to predict such activities (A,B,C,D,E).