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Python Linear Regression Quiz
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
There are 25 questions to complete.
