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‘linear regression’ Interview Questions

What problems would arise from using a regular linear regression to model a binary outcome?

What is non-negative least squares, and when is it used?

What are potential problems encountered in Linear Regression?

What is a high influence point?

What is a high leverage point?

What is an outlier?

What is the difference between outliers, high leverage points, and high influence points?

What is the difference between Regression and ANOVA?

Why does multicollinearity result in poor estimates of coefficients in linear regression?

Doesn’t polynomial regression violate the multicollinearity assumption for Linear Regression?

What are some approaches for modeling non linear relationships?

Differentiate between Linear Models and Non Linear Models

What are the most common transformations when the target variable is not normally distributed?

How can qualitative predictors be incorporated in linear regression?

Suppose there are a large number of predictors ‘p’. What is the best approach to find out if any of the p predictors are helpful in predicting the response ‘y’?

What are some of the problems with stepwise selection approaches?

What is Information Criteria (AIC, BIC)?

What are the various measures of error (MSE, RMSE, MAE)?

What is R-squared and adjusted R-squared?

What is Global F-Test?

What are some of the evaluation criteria used to assess a Linear Regression model?

What is multicollinearity and how can that be identified?

How is variability measured in Linear Regression?

How are coefficients of linear regression estimated?

What are some methods of Variable Selection?

What are the assumptions of linear regression?

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‘linear regression’ Interview Questions