An investigation entitled "On Some Aspects of Validation Techniques" was conducted on simulated data. Various linear and non–linear statistical models were used in this study. Simulated data of 200 observations were generated in R studio with respect to symmetric and asymmetric distribution, further, these models were then fitted on the simulated data. The aim of this study was to evaluate different validation techniques and assess their suitability for the predictive performance of various linear and non-linear statistical models, as validation is a technique for assessing how the results of a statistical model will generalize to an independent data set and is mainly used in settings where the goal is prediction. One wants to estimate how accurately a prediction model will perform in practice.
This study can be a benchmark for policymakers, as formulation and initiation of economic policy and planning become easy if data sets are analyzed in advance which requires fitting and validation of various statistical models.