Abstract
Simple linear regression can be very helpful for exploring a dataset through identifying variable relationships and the percentage of variance in the dependent variable explained by the model. The project showcases a coding package for the statistical programming language R that I have created to provide a streamlined approach to exploring data through linear regression. Packages serve as extensions of coding languages to add features or functions. This package automates simple linear regression and outputs key values for data exploration such as p value, R^2, and independent variable coefficients in an organized and concise manner. In addition to regression, this package includes variance inflation factor (VIF) analysis to detect multicollinearity and flag it in the output. This enables quantitative researchers to be more organized and thus spend less time coding during their pursuit of statistical insights.
Included in
Statistical Programming Package for Simplified Data Exploration
Simple linear regression can be very helpful for exploring a dataset through identifying variable relationships and the percentage of variance in the dependent variable explained by the model. The project showcases a coding package for the statistical programming language R that I have created to provide a streamlined approach to exploring data through linear regression. Packages serve as extensions of coding languages to add features or functions. This package automates simple linear regression and outputs key values for data exploration such as p value, R^2, and independent variable coefficients in an organized and concise manner. In addition to regression, this package includes variance inflation factor (VIF) analysis to detect multicollinearity and flag it in the output. This enables quantitative researchers to be more organized and thus spend less time coding during their pursuit of statistical insights.