Statistics

If two predictors are highly correlated, what is the effect on the coefficients in the logistic regression? What are the confidence intervals of the coefficients?

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Behavioral
  • If two predictors are highly correlated, what is the effect on the coefficients in the logistic regression? What are the confidence intervals of the coefficients?
  • Have you ever encountered a situation where two predictor variables were so highly correlated that they caused any problems in your logistic regression analysis?
  • Does the high correlation between two predictor variables have any effect on logistic regression coefficients? If so, what might be the implications?
  • How does multicollinearity impact the coefficients in logistic regression? Specifically, what happens when two predictors are highly correlated?
  • In logistic regression, if two predictors are highly correlated, what impact does this have on the estimates of the regression coefficients and their confidence intervals?
  • Can you explain in simpler terms how the collinearity between two predictors could influence the coefficients in logistic regression?
  • What are the assumptions we make about our predictor variables when we fit logistic regression models? Does this include making sure there's no collinearity between our predictors?
  • Does using highly correlated predictor variables in logistic regression affect our ability to make sound statistical conclusions? How do we handle that?
  • How does the magnitude of correlation between two type predictors affect the regression coefficients and the confidence intervals in logistic regression?
  • When two predictors are highly correlated in logistic regression, what happens to the coefficients and their confidence intervals?

Interview question asked to Data Scientists interviewing at UberEats, Roku, Swiggy and others: If two predictors are highly correlated, what is the effect on the coefficients in the logistic regression? What are the confidence intervals of the coefficients?.