Which Python library is primarily used for numerical computing and provides support for arrays and matrices, essential for Linear Regression calculations?
pandas
scikit-learn
matplotlib
NumPy
What is the primary goal of feature selection in linear regression?
Maximize the number of features used in the model
Introduce bias into the model
Increase the complexity of the model
Improve the model's interpretability and reduce overfitting
What graphical tool is commonly used to visualize the relationship between two continuous variables in linear regression?
Pie chart
Bar chart
Scatter plot
Histogram
What function from scikit-learn is used to perform Linear Regression?
linear_model.LinearRegression()
preprocessing.StandardScaler()
model_selection.train_test_split()
linear_model.LogisticRegression()
What type of visualization tool is commonly used to initially assess the relationship between two continuous variables in linear regression?
If a Durbin-Watson test statistic is close to 2, what does it suggest about the residuals?
They exhibit a linear pattern
They are independent
They are normally distributed
They are homoscedastic
Who is credited with developing the foundational principles of linear regression?
Albert Einstein
Marie Curie
Isaac Newton
Sir Francis Galton
Backward elimination in linear regression involves removing features based on what criterion?
The feature with the highest correlation with the target variable
The feature with the lowest p-value
The feature that contributes the least to multicollinearity
The feature that results in the smallest decrease in model performance
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.
Forward selection is used for classification, while backward elimination is used for regression.
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.
What does a high R-squared value indicate?
The independent variables are not correlated with the dependent variable.
The model is a perfect fit for the data.
A large proportion of the variance in the dependent variable is explained by the independent variables.
The model is not a good fit for the data.