the regressions of least absolute shrinkage and selection operator with applications

Lasso regression model is a causal model based on providing a more accurate estimator through the model’s dependence on shrinkage, in which the data values were reduced towards the data center. this model solves the problems of multicollinearity presence of high relationships between the explanatory variables of the model. in this research, a number of factors (sample size, number of explanatory variables and pollution rate) were adopted in order to observe the ability of these factors to effect lasso regression.