Fundamental Types of Regression Analysis on Various Research Data using R Programming

Abstract

The goal of this analytical review paper is for discussing the relationship between various types of regression analysis methods whose output were sufficiently analyzed using R programming. The regression analysis is calculated with three different case studies of different datasets for explaining linear and multiple regressions. Similarly, polynomial regression analysis is calculated with 2955 observation and 8 attributes of Florida date set whose residual standard error is calculated with 11520 on 2949 degrees of freedom, multiple R-squared is 0.07828, the adjusted R-squared is 0.07672 and F-statists is 50.09 on 5. Likewise, the quintal regression analysis is carried out through binary data sets of 20 observations of 4 attributes whose, AIC value is fit between two or more models at 26 percentages and 75 percent accuracy. The primary purpose of this paper is to explain the relationship of linear, multiple, quantile and polynomial regression models to achieve final conclusion with different data sets. Therefore, this paper presents easiest way of fundamental types of regression analysis commands and R programming strengths for of data analysis.