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BUSINESS ANALYTICS METHOD AND SOFTWARE - Coursework Example

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Over the past two decades, the world of data mining and analytics has been revolutionized due to the advances in statistical software for the targeted discovery of patterns and knowledge from large amounts of data. The use of data mining is widespread across several domains such…
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BUSINESS ANALYTICS METHOD AND SOFTWARE
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Setting up complex statistical analyses on large data without prior identification of the objectives and knowhow about the suitability and possible outcome of the proposed analyses often renders misleading results. According to Albert Einstein, “the formulation of a problem is often more essential than its solution which may be merely a matter of mathematical or experimental skill” (Faraway, 2002). This report aims to explore the utility of statistical software such as R and gain insights into the statistical methods such as multivariate analysis of variance (MANOVA) with as well as without regard to each other.

The report will provide with a comprehensive overview of the R software, its advantages and disadvantages, current market trends in the software category and reflect hands-on experience gained by its use. Next, the report will provide with a comprehensive overview of MANOVA, and advantages and disadvantages associated with it. Finally, the report will include a step-by-step description on the implementation of MANOVA in R, followed by the conclusions. R is a computer scripting language and an interactive software environment designed particularly for statistical analyses, manipulation and visualization of data and results (Seefeld, 2007; Venables et al., 2008). The name, R, was used by Robert Gentleman and Ross Ihaka, while creating the R project at the Department of Statistics, University of Auckland, in 1995 (Owen, 2010).

The language was mostly derived from two existing languages, S and Scheme, developed in 1985 and 1975, respectively. While addressing the issues involved in the design and implementation of these languages for statistical computing, the authors considered combining their strengths to produce another language. The resulting language, R, largely resembles S but is based on semantics and

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