A presentation for the Shiny MTCars application, which you can use to create linear regression models on the Motor Trend Car Road Tests (mtcars) dataset for certain response variables.

From Eisenhower to Obama.

Tiny exercise of Leaflet and Rmd presentation.

In this paper I do classification of weight lifting exercises. I try out two different types of models, random forests (RF) and gereralized boosting regression model (GBM) with different parameters to find sufficient model(s) for prediction. Prediction is preceded by data preparation.

This analysis uses linear regression models on the Motor Trend Car Road Tests (mtcars) dataset to identify whether or not there is any impact of a car’s transmission type (automatic or manual) on how far it can travel with one US gallon of fuel. First we overview the mtcars dataset, do some minor data transformations and explore the relevant part of the dataset, then we thoroughly analyse the possible relationship between miles per gallon values and transmission types using several regression models and a simple model selection algorithm, and conclude that in practice the transmission type has no statistically significant impact on MPG.

This analysis gives a simple example of the Central Limit Theorem (CLT) in work. First we do a simulation where a bunch of samples are drawn from an exponential distribution, then we take the mean of these samples, and investigate the distribution of these (i.e. the sampling distribution of sample means).

This analysis takes the ToothGrowth dataset from the R package datasets, which contain data about effects of vitamin C on tooth growth of guinea pigs. This analysis in particular focuses on the effect of delivery method: Is there a difference in the effect of vitamin C on tooth growth in guinea pigs when vitamin C is delivered by ascorbic acid and by orange juice?

This analysis uses the U.S. National Oceanic and Atmospheric Administration’s (NOAA) Storm Database to identify weather events most harmful to health and economy.

This is the first publication within the Reproducible Research course on Coursera.