What question did this study set out to answer?

To review and evaluate primary diagnostic techniques for identifying multicollinearity and its consequences within multiple linear regression models.

June 15, 2020American Journal of Applied Mathematics and StatisticsOpen Access

Detecting Multicollinearity in Regression Analysis

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To review and evaluate primary diagnostic techniques for identifying multicollinearity and its consequences within multiple linear regression models.
Analyzed customer satisfaction questionnaire survey data using multiple linear regression frameworks.
Tested three diagnostic multicollinearity detection methods: pairwise correlation coefficients, variance inflation factors (VIF), and the eigenvalue method.
Evaluated advanced alternative regression methods, including principal components regression, weighted regression, and ridge regression.
Multicollinearity masked genuine predictor significance, causing meaningful variables to appear statistically non-significant.
Product attractiveness was identified as a more rational and dominant driver of customer satisfaction than other evaluated predictors.

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Implication

Survey analysis demonstrates methods for detecting multicollinearity in customer satisfaction data, highlighting product attractiveness as the primary predictor over correlated variables.

Cite This Study

Noora Shrestha (2020) studied this question.

synapsesocial.com/papers/69d770d2086f9d6299f31015 https://doi.org/10.12691/ajams-8-2-1

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Implication

Survey analysis demonstrates methods for detecting multicollinearity in customer satisfaction data, highlighting product attractiveness as the primary predictor over correlated variables.

Detecting Multicollinearity in Regression Analysis

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Discussion

Implication

Cite This Study

Detecting Multicollinearity in Regression Analysis

Key Points

Discussion

Implication

Cite This Study

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