Which of these methods can be used to address heteroscedasticity?
All of the above
Removing outliers
Transforming the dependent variable
Adding more independent variables
What is the method used in linear regression to estimate the model parameters that minimize the sum of squared errors?
Least Squares Estimation
Bayesian Estimation
Maximum Likelihood Estimation
Method of Moments
What does the 'fit_intercept' parameter in 'LinearRegression()' control?
Whether to calculate the slope of the line.
Whether to normalize the data before fitting.
Whether to calculate the intercept (bias) of the line.
Whether to use gradient descent for optimization.
What is a potential drawback of using a purely automated feature selection technique (like forward selection or backward elimination) without careful consideration?
It can sometimes overlook features that might be important in combination with others.
It can lead to models that are less accurate than using all available features.
It guarantees the most interpretable model.
It completely eliminates the need for domain expertise in model building.
What does the linearity assumption in linear regression imply?
The dependent variable must have a normal distribution.
The relationship between the dependent and independent variables can be best represented by a straight line.
The independent variables are unrelated to each other.
The data points are evenly distributed around the regression line.
How does the Mean Squared Error (MSE) penalize larger errors compared to smaller errors?
It uses a logarithmic scale to compress larger errors.
It takes the absolute value of the errors, ignoring the sign.
It squares the errors, giving more weight to larger deviations.
It doesn't; all errors are penalized equally.
Which of these is a common visual tool for diagnosing heteroscedasticity?
Box plot
Histogram
Normal probability plot
Scatter plot of residuals vs. predicted values
What does a residual represent in linear regression?
The predicted value of the dependent variable.
The slope of the regression line.
The difference between the actual and predicted values of the dependent variable.
The intercept of the regression line.
What is the main difference between forward selection and backward elimination in linear regression?
Forward selection starts with no features and adds one by one, while backward elimination starts with all features and removes one by one.
There is no difference; both techniques achieve the same outcome.
Forward selection starts with all features and removes one by one, while backward elimination starts with no features and adds one by one.
Forward selection is used for classification, while backward elimination is used for regression.
If a Durbin-Watson test statistic is close to 2, what does it suggest about the residuals?
They are homoscedastic
They are normally distributed
They are independent
They exhibit a linear pattern