Titlebook: R for Business Analytics; A Ohri Book 2013 Springer Science+Business Media New York 2013 Business Analytics.Data Mining.Data Visualization

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before 1900 the limitations of the batteries to drive them have not enabled them to compete in the general consumer’s market with the internal combustion engine. Automotive parts are limited by space and weight. Therefore EVs and hybrid electric vehicles (HEVs) require a battery with high energy den

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Exploring Data,t the process of looking at slices of data for generating various insights. We will emphasize the need for data visualization both as an acknowledgement of growing demands of data volume and easy understandability by business audiences. The fact that R currently has one of the most advanced graphica

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Building Regression Models, goods and services. Within the R Project, regression packages are shown in the documentation in both the Econometrics view—.—and the Finance view. A basic summary of all the R functions used for building regression models can be seen at .. A very good textbook on the basics of regression is . by Ju

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Clustering and Data Segmentation,ging to one group or cluster are more similar to each other and more different from objects in another group or cluster. Clustering is used in business analytics to identify groups of customers that can be targeted with similar products, to understand products and markets, and basically to reduce da

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Optimizing R Code,nalyst, well-designed GUID tools that are stable to use, pull data and models, and report them are essential, and all these are available within various R subcomponents and packages. This chapter is aimed at analysts wishing to tweak their overall R experience by measuring R performance and improvin

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, references, interviews and useful links.Background informa.R for Business Analytics. looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages.  With this information the reader can select the packages tha

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Exploring Data,nt of growing demands of data volume and easy understandability by business audiences. The fact that R currently has one of the most advanced graphical libraries also helps. We will be using basic graphical capabilities but will also briefly touch on advanced customization using the acclaimed ggplot2 package.