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‘Linear Regression’ Interview Questions

What is Regression?

Differentiate between Linear Models and Non Linear Models

What is Linear Regression?

How are coefficients of linear regression estimated?

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

What are the assumptions of linear regression?

How can qualitative predictors be incorporated in linear regression?

What are potential problems encountered 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 approaches for modeling non linear relationships?

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

What is multicollinearity and how can that be identified?

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

What is R-squared and adjusted R-squared?

What is Global F-Test?

What is Information Criteria (AIC, BIC)?

What are some methods of Variable Selection?

What are some of the problems with stepwise selection approaches?

How is variability measured in Linear Regression?

What is the difference between Regression and ANOVA?

What is an outlier?

What is a high leverage point?

What is a high influence point?

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

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

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‘Linear Regression’ Interview Questions