1Division of Agricultural Statistics, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar, Jammu and Kashmir (India)
2Division of Agricultural Statistics, SKUAST-K, Shalimar
3Institute of Business Policy and Research, SKUAST-K, Shalimar
4Division of Plant Pathology, SKUAST-K, Wadura
*e-mail: uzmamajeed@skuastkashmir.ac.in
Online published on 30 September, 2025.
Multicollinearity poses a challenge in regression analysis, leading to unstable estimates of regression coefficients and complicating the interpretation of explanatory variables. This study addressed the issue of multicollinearity in the context of multiple linear regression (MLR) using principal components regression. The findings underscored the efficacy of principal component regression in addressing multicollinearity using performance criteria with R2 of 0.946, RMSE of0.576, AIC of 21.227 and BIC of 23.818.
Multicollinearity, Multiple linear regression, Performance criteria, Principal components regression
