Statistics

What is multicollinearity?

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Anonymous

6 months ago
3.6Strong
Multicollinearity between features is when 2 or more independent features are correlated. Certain models such as linear regression assumes that underlying data has no Multicollinearity. We can use VIF to see if there is any Multicollinearity in the data. we can remove features with VIF value >5 to address Multicollinearity
  • Why is multicollinearity a problem in regression models?
  • What is multicollinearity?
  • Can you define multicollinearity and provide an example of how it can impact regression analysis results?
  • How do you identify multicollinearity in a dataset?
  • Can you discuss some of the consequences of ignoring multicollinearity in regression analysis?
  • How do you address multicollinearity in regression models?
  • What are some common methods for detecting and dealing with multicollinearity?
  • Have you ever encountered multicollinearity in your own work? How did you address it?
  • What is the impact of multicollinearity on the accuracy of regression analysis?
  • In what ways can multicollinearity affect the interpretation of regression coefficients?
  • Explain the concept of multicollinearity in statistical analysis.
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Interview question asked to Data Scientists interviewing at Prezi, Apple, Deloitte and others: What is multicollinearity?.