The "Shiny Predictive Text App" (https://asafilian.shinyapps.io/as_txtpredict) is a web-based application suggesting words the end user may wish to insert in a text field. The current report describes the technical aspect of the product.

A presentation of what the application is, and how it works.

In this project, we aim at building a predictive text product that makes it easier for people to type. The current report addresses the first phase of the project, where we get familiar with the databases and do the necessary cleaning and preliminary analysis.

This project makes use of data from a personal activity monitoring device. The data consists of two months of data from an anonymous individual collected during the months of October and November, 2012 and include the number of steps taken in 5 minute intervals each day. We address the following questions in this report: * What is mean total number of steps taken per day? * What is the average daily activity pattern? * Are there differences in activity patterns between weekdays and weekends?

In this project, we use data of 6 participants. They were asked to perform barbell lifts correctly and incorrectly in 5 different ways. More information is available on http://groupware.les.inf.puc-rio.br/har. The goal of this project is to predict the manner in which they did the exercise.

MPG analysis for Automatic and Manual Transmission via Regression Models

This is a presentation of a Shiny application. This project was submitted as the final assingment for the Coursera course Developing Data Products.

In this presentation, we see a 3D plot for the ChickWeight data set. This document was submitted as the second assignment of the Coursera course "Developing Data Products" course.

An interactive map of top 10 universities in Ontario, Canada.

We analyze the ToothGrowth data in the R datasets package. The data is about the effect of vitamin C on tooth growth in guinea pigs. In this data, ``the response is the length of odontoblasts (cells responsible for tooth growth) in 60 guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, orange juice (coded as OJ) or ascorbic acid (a form of vitamin C and coded as VC).’’ We will further explore the data in the next section. We provide some basic summary analyses of the data. We also provide some exploratory analyses. Moreover, we provide the density distributions of the growth length for all combinations of dose levels and delivery methods. Finally, we have done a multiple hypothesis tests.

We show that the distribution of averages of exponentially distributed variables becomes that of a standard normal as the sample size increases (according to the Central Limit Theorem (CLT)).

In this article, we explore the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database, which represents characteristics of major weather events in the US. The charateristics include when and where the events occur and estimates of any health damage (i.e., fatalities and injuries) and economic damages (i.e., property and crop damages). The research questions we have addressed in this analysis are as follows: (1) Across the United States, which types of events are most harmful with respect to population health? (2) Across the United States, which types of events have the greatest economic consequences?