Python Linear Regression Quiz

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Question 1

In the context of linear regression, what is the purpose of feature scaling?

• To improve model interpretability

• To handle multicollinearity

• To speed up the training process

• To make coefficients comparable

Question 2

How does heteroscedasticity impact the results of linear regression?

• It inflates standard errors and can lead to incorrect inferences

• It improves the precision of coefficient estimates

• It has no impact on the regression results

• It reduces bias in the model

Question 3

In linear regression, what does a negative coefficient for an independent variable imply?

• Positive relationship with the dependent variable

• No relationship with the dependent variable

• Negative relationship with the dependent variable

• Inverse relationship with the dependent variable

Question 4

How does the regularization parameter in Lasso regression differ from Ridge regression?

• Lasso has a separate regularization parameter for each coefficient

• Lasso uses the same regularization parameter for all coefficients

• Ridge has a separate regularization parameter for each coefficient

• Ridge uses the same regularization parameter for all coefficients

Question 5

In quantile regression, what does the choice of quantile represent?

• Intercept of the regression line

• Slope of the regression line

• Level of the conditional distribution

• Coefficient of determination (R-squared)

Question 6

In Ridge regression, what happens to the regularization term as the hyperparameter (alpha) increases?

• It decreases

• It increases

• It remains constant

• It becomes irrelevant

Question 7

When using polynomial regression, what does increasing the degree of the polynomial imply?

• Decreased model flexibility

• Increased model complexity

• Reduced risk of overfitting
  

• Limited capability to capture nonlinearity

Question 8

How does the learning rate impact the convergence of the gradient descent algorithm in linear regression?

• Higher learning rates lead to faster convergence

• Lower learning rates lead to faster convergence

• Learning rate has no impact on convergence

• Learning rate affects only the model's accuracy

Question 9

How does the presence of outliers impact the coefficient estimates in linear regression?

• It inflates standard errors and can lead to biased estimates

• It improves the precision of coefficient estimates

• It has no impact on coefficient estimates

• It reduces bias in the model

Question 10

What is the purpose of cross-validation in the context of linear regression?

• To assess model performance on new data

• To improve model interpretability

• To test the assumption of homoscedasticity

• To check for multicollinearity

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