Optimizing Transformation for Linearity between Online Software Repository Variables.

Ogunyinka, Peter I, Badmus, Nofiu Idowu


Online Software Repositories (OSR) like sourceforge.net and google code contain a wealth of valuable data about software projects but these data violate the linearity and normal assumptions, hence making the data impossible for use in most statistical data analysis. To prepare these data for statistical data analysis, the data were non-linearly transformed, hence, these research established the best twelve (12) transformed model that obey linearity assumptions, higher coefficient of determination ( ), positive and negative relationship and gained variable significance over the original data. Similarly, the back transformation or interpretation was provided about each of these twelve (12) best ranked linear models to solve the challenges of data transformation encountered by researchers.

Keywords: Data transformation, Linear regression model, OkikiSoft, Online Software Repository and Sourceforge.net.

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ISSN (Paper)2224-5804 ISSN (Online)2225-0522

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