What does a high R-squared value indicate?
A large proportion of the variance in the dependent variable is explained by the independent variables.
The model is a perfect fit for the data.
The model is not a good fit for the data.
The independent variables are not correlated with the dependent variable.
What does the 'fit_intercept' parameter in 'LinearRegression()' control?
Whether to use gradient descent for optimization.
Whether to calculate the intercept (bias) of the line.
Whether to normalize the data before fitting.
Whether to calculate the slope of the line.
Which of the following is NOT a benefit of feature selection in linear regression?
Improved model interpretability
Potential for better generalization to new data
Reduced computational cost
Increased risk of overfitting
What does a residual represent in linear regression?
The slope of the regression line.
The intercept of the regression line.
The predicted value of the dependent variable.
The difference between the actual and predicted values of the dependent variable.
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.
Who is credited with developing the foundational principles of linear regression?
Sir Francis Galton
Marie Curie
Isaac Newton
Albert Einstein
In the context of linear regression, what is an error term?
The variation in the independent variable.
The difference between the slope and the intercept of the regression line.
The difference between the observed value of the dependent variable and the predicted value.
A mistake made in collecting or entering data.
What graphical tool is commonly used to visualize the relationship between two continuous variables in linear regression?
Bar chart
Histogram
Scatter plot
Pie chart
Which of these is a common visual tool for diagnosing heteroscedasticity?
Box plot
Scatter plot of residuals vs. predicted values
Normal probability plot
What does a pattern in the residual plot suggest?
The residuals are normally distributed.
The linear model is a good fit for the data.
There is no relationship between the independent and dependent variables.
The linear model is not a good fit for the data, and a non-linear model may be more appropriate.