This application is based on the U.S. National Oceanic and Atmospheric Administration's (NOAA) storm database. This presentation is created as part of the curriculum of the course "Developing Data Products" offered by John Hopkins University. This application was created by Montassar H'daya on September 26,2015 to show the impacts of US disasters which occured between 1950 and 2011.

This application is based on the U.S. National Oceanic and Atmospheric Administration's (NOAA) storm database. This presentation is created as part of the curriculum of the course "Developing Data Products" offered by John Hopkins University. This application was created by Montassar H'daya on September 26,2015 to show the impacts of US disasters which occured between 1950 and 2011.

This detailed analysis has been performed to fulfill the requirements of the course project for the course Regression Models offered by the Johns Hopkins University on Data science specialization. In this project, we will analyze the mtcars data set and explore the relationship between a set of variables and miles per gallon (MPG) which will be our outcome. The main objectives of this research are as follows Is an automatic or manual transmission better for MPG? Quantifying how different is the MPG between automatic and manual transmissions?

This project investigates the exponential distribution in R and compare it with the Central Limit Theorem. The exponential distribution is simulated with rexp(n, lambda) where lambda is the rate parameter, theoretical mean of exponential distribution is 1/lambda and theoretical standard deviation is also 1/lambda. This project performs a thousand simulations to get the distribution of averages of 40 exponentials, where the lambda is set to 0.2 for all of the simulations

This analysis is to determine which type of events is the most harmfull and the effect of that events on economic consequences. We will use the estimates of fatalities, injuries, property and crop damage to decide which types of event are most harmful to the population health and economy. From these data, we found that excessive heat and tornado are most harmful with respect to population health, while flood, drought, and hurricane/typhoon have the greatest economic consequence

This analysis is to determine which type of events is the most harmfull and the effect of that events on economic consequences. We will use the estimates of fatalities, injuries, property and crop damage to decide which types of event are most harmful to the population health and economy. From these data, we found that excessive heat and tornado are most harmful with respect to population health, while flood, drought, and hurricane/typhoon have the greatest economic consequence

This analysis is to determine which type of events is the most harmfull and the effect of that events on economic consequences. We will use the estimates of fatalities, injuries, property and crop damage to decide which types of event are most harmful to the population health and economy. From these data, we found that excessive heat and tornado are most harmful with respect to population health, while flood, drought, and hurricane/typhoon have the greatest economic consequence

It is now possible to collect a large amount of data about personal movement using activity monitoring devices such as a Fitbit, Nike Fuelband, or Jawbone Up. These type of devices are part of the "quantified self" movement -- a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. But these data remain under-utilized both because the raw data are hard to obtain and there is a lack of statistical methods and software for processing and interpreting the data. This assignment makes use of data from a personal activity monitoring device. This device collects data at 5 minute intervals through out the day. 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.

It is now possible to collect a large amount of data about personal movement using activity monitoring devices such as a Fitbit, Nike Fuelband, or Jawbone Up. These type of devices are part of the "quantified self" movement -- a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. But these data remain under-utilized both because the raw data are hard to obtain and there is a lack of statistical methods and software for processing and interpreting the data. This assignment makes use of data from a personal activity monitoring device. This device collects data at 5 minute intervals through out the day. 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.