Prediction of the next word

he project consists of two parts: A simulation exercise. Basic inferential data analysis. You will create a report to answer the questions. Given the nature of the series, ideally you'll use knitr to create the reports and convert to a pdf. (I will post a very simple introduction to knitr). However, feel free to use whatever software that you would like to create your pdf. Each pdf report should be no more than 3 pages with 3 pages of supporting appendix material if needed (code, figures, etcetera).

Elimane NDOYE 26/06/2019 Purpose ;The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. 2 main questions that will be addressed in this analysis: Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health? Across the United States, which types of events have the greatest economic consequences?

Summary This assigment aims at providing the R code required for plotting 4 pre-defined plots

his document is the final report of the Peer Assessment project from the Practical Machine Learning course, which is a part of the Data Science Specialization. It was written and coded in RStudio, using its knitr functions and published in the html format. The purpose of this analysis is to predict the manner in which the six participants performed the exercises described below and to answer the questions of the associated course quiz. The machine learning algorithm, which uses the classe variable in the training set, is applied to the 20 test cases available in the test data. The predictions are submitted to the Course Project Prediction Quiz for grading. 2. Introduction

Motor Trend, an automobile magazine proposed to analyze the relationship between a set of variables and miles per gallon (MPG) outcome. We will analyze the mtcars dataset from the 1974 Motor Trend US magazine to answer the following questions: -How automatic versus manual transmission influences the miles per gallon (MPG) levels? -What MPG differences can be measured between automatic and manual transmissions? We will use the simple linear regression analysis, in order to determine whether there is a signficant difference between the mean MPG for automatic and manual transmission cars. Manual transmissions achieve a higher value of MPG compared to automatic transmission. This increase is approximately 1.8 MPG when switching from an automatic transmission to a manual one, with all else held constant.

Motor Trend, an automobile magazine proposed to analyze the relationship between a set of variables and miles per gallon (MPG) outcome. We will analyze the mtcars dataset from the 1974 Motor Trend US magazine to answer the following questions: -How automatic versus manual transmission influences the miles per gallon (MPG) levels? -What MPG differences can be measured between automatic and manual transmissions? We will use the simple linear regression analysis, in order to determine whether there is a signficant difference between the mean MPG for automatic and manual transmission cars. Manual transmissions achieve a higher value of MPG compared to automatic transmission. This increase is approximately 1.8 MPG when switching from an automatic transmission to a manual one, with all else held constant.

Motor Trend Car Road Tests The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973--74 models). Keywords datasets Usage mtcars Note Henderson and Velleman (1981) comment in a footnote to Table 1: ‘Hocking [original transcriber]'s noncrucial coding of the Mazda's rotary engine as a straight six-cylinder engine and the Porsche's flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.’ Format A data frame with 32 observations on 11 (numeric) variables.

Assignment: Caching the Inverse of a Matrix Matrix inversion is usually a costly computation and there may be some benefit to caching the inverse of a matrix rather than compute it repeatedly (there are also alternatives to matrix inversion that we will not discuss here). Your assignment is to write a pair of functions that cache the inverse of a matrix. Write the following functions: makeCacheMatrix: This function creates a special "matrix" object that can cache its inverse. cacheSolve: This function computes the inverse of the special "matrix" returned by makeCacheMatrix above. If the inverse has already been calculated (and the matrix has not changed), then the cachesolve should retrieve the inverse from the cache. Computing the inverse of a square matrix can be done with the solve function in R. For example, if X is a square invertible matrix, then solve(X) returns its inverse. For this assignment, assume that the matrix supplied is always invertible.

Assignment: Caching the Inverse of a Matrix Matrix inversion is usually a costly computation and there may be some benefit to caching the inverse of a matrix rather than compute it repeatedly (there are also alternatives to matrix inversion that we will not discuss here). Your assignment is to write a pair of functions that cache the inverse of a matrix. Write the following functions: makeCacheMatrix: This function creates a special "matrix" object that can cache its inverse. cacheSolve: This function computes the inverse of the special "matrix" returned by makeCacheMatrix above. If the inverse has already been calculated (and the matrix has not changed), then the cachesolve should retrieve the inverse from the cache. Computing the inverse of a square matrix can be done with the solve function in R. For example, if X is a square invertible matrix, then solve(X) returns its inverse. For this assignment, assume that the matrix supplied is always invertible.

{r setup, include=FALSE} makeCacheMatrix <- function(x, ...) { m <- x$getmean() if(!is.null(m)) { message("getting cached data") return(m) } data <- x$get() m <- mean(data, ...) x$setmean(m) m } cacheSolve <- function(x, ...) { m <- x$getmean() if(!is.null(m)) { message("getting cached data") return(m) } data <- x$get() m <- mean(data, ...) x$setmean(m) m } ## This function creates a special "matrix" object that can cache its inverse makeCacheMatrix <- function(x = matrix()) { m <- NULL # Set matrix elements.

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Create a Line Graph with R Markdown

Create a Line Graph with R Markdown

Create a line Graph with R Markdown

Create a plot with R Marldown & Plotly

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Create a map for Mooc assignment

Create a web page presentation using R Markdown that features a plot created with Plotly.