Machine Learning Algorithm: Multiple Linear Regression Analysis on Employee Job Performance using R-Programming

Abstract

Aim/Purpose:- The aim of empirical research study was to know the impact of independent variables like: Training and Development, Performance Appraisal, Employee Compensation and Employee Job Satisfaction on Employee Job-Performance. The assessment of employee Job-Performance is in evitable, because the produictivity of the organization dependents on employee job performance. Outcome:- The outcome of the research will be, list out the factors which will impact on employee job performance in contemporary world. Research Design:- Used structured research instrument to collect the data from 500 sample respondents. Research Validity:- The data validated with the help of Cronebach’s alpha Reliability test (>.79) followed by the loaded factors validated through confirmatory factor analysis(>.50) and Model validated through CFA (GFI>.90, AGFI>.90, TLI>.90, RMSEA<.08) and other aspects. Research Tools:- There are various statistical tools applied to analyze the data like: Path Analysis, confirmatory factor analysis, correlation and Regression other reliability (Convergent and composite) concepts.  The outcome of the research revealed that, there is a considerable impact of control and main variables impact on outcome of Employee Job performance.. Surprisingly, the control variables impact dominated the main variables effect on overall outcome of the research. Generalizability:- The study strengthened with extensive literature survey including western and Indian studies. The outcome of the research can be generalized under any circumstances and conditions. Novelty:- The model is new which is not created earlier and it will create new insight to the readers in the competitive insight. Apart from findings of the study will helpful to the organizations for better understanding of employee requirements which facilitate organization prosperity in the competitive edge.