This paper by Noora Shrestha discusses the detection of multicollinearity in regression analysis using questionnaire survey data on customer satisfaction. Multicollinearity occurs when multiple independent variables are highly correlated with each other, leading to statistically insignificant results. The study employs three primary techniques to detect multicollinearity: correlation coefficients, variance inflation factor (VIF), and eigenvalue method. The survey was conducted among 310 young smartphone users, and the variables included product quality, brand experience, product feature, product attractiveness, and product price. The results show that while there is a positive correlation between these variables and customer satisfaction, there is no evidence of multicollinearity. The variable "product attractiveness" is identified as the most significant factor influencing customer satisfaction. Advanced regression procedures such as principal components regression, weighted regression, and ridge regression are suggested for further research to better understand the relationship between customer satisfaction and these factors.This paper by Noora Shrestha discusses the detection of multicollinearity in regression analysis using questionnaire survey data on customer satisfaction. Multicollinearity occurs when multiple independent variables are highly correlated with each other, leading to statistically insignificant results. The study employs three primary techniques to detect multicollinearity: correlation coefficients, variance inflation factor (VIF), and eigenvalue method. The survey was conducted among 310 young smartphone users, and the variables included product quality, brand experience, product feature, product attractiveness, and product price. The results show that while there is a positive correlation between these variables and customer satisfaction, there is no evidence of multicollinearity. The variable "product attractiveness" is identified as the most significant factor influencing customer satisfaction. Advanced regression procedures such as principal components regression, weighted regression, and ridge regression are suggested for further research to better understand the relationship between customer satisfaction and these factors.