The objective of this exercise is to model the Weightlifting Exercise data available at http://groupware.les.inf.puc-rio.br/har to predict in which one out of the 5 different fashions the subjects in the test set were exercising. According to the site, 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). Sensors were fit to their arm, forearm, dumbbell and belt and several parameters recorded. We are to develop a model based on the training data set with all measurements and output class, and have to predict the output class for the data in the test set.

Digital Marketing increasingly uses Social Media for campaigns. The article shows how public data can be scraped and analysed using R to develop interesting insights about page engagements of competing brands.

We examine the **mtcars** data set of the 1974 *Motor Trend* magazine to investigate if automatic or manual transmission might be better for mpg. By data exploration and then rigorous statistical inference and regression, we conclude that manual transmission is better, but there are other factors too affecting mileage.

In this study we investigate the behaviour of sample averages drawn from the Exponential Distribution and examine how it matches the predictions of the Central Limit Theorem.

The objective of this treatise is to report the exploratory and statistical analysis of the ToothGrowth data in the R datasets package. For this, we have: * Loaded the ToothGrowth data and provided a basic summary of the data * Performed some Exploratory Data Analyses, * Performed some Statistical Analyses based on t-hypothesis tests and t- confidence intervals to compare tooth growth by supplement and dose, and * Stated our conclusions and the assumptions needed for the conclusions.

We explore the National Oceanic and Atmospheric Administration’s (NOAA) Storm Events Database and try to find out the severe weather events in the US that cause the maximum Human distress and Financial damage.