This is the presentation for the Swiftkey project, capstone for the Data Science Specialization by Johns Hopkins University.

This is a milestone report for the ‘Data Science Capstone’ of the course Data Science Specialization, by Johns Hopkins University. It is being requested to create an application of Predictive Text Model, capable of predicting subsequent words and which will be trained with a dataset from blogs, Twitter and news. In this report, an exploratory data analysis is carried out and the design of the future application will be described.

Shiny app developed for the assignment of course Developing Data Products of Johns Hopkins University Aimed to help on solving the question if an automatic or manual transmission is better for fuel consumption, techniques of regression analysis are solved. - Linear regressor with lm() function, where variables are dynamically added - Correlation anaysis between the variables with cor() function in a nXn matrix - The weighted residuals, the usual residuals rescaled by the square root of the weights specified in the call to lm. The user of the app can instantly verify the effect of adding additional variables to the analysis. The code is available in https://github.com/rmmoya/regression_analysis_shinyapp

This web page is developed as an exercise for the course of "Developing Data Products" from Johns Hopkins Univerisity.

Course project of Reproducible Research, part of the Data Science specialization offered by Johns Hopkins University in Coursera.