Which of the following is the general equation for a simple linear regression model?
y = e^(b0 + b1*x)
y = b0 * x^b1
y = b0 + b1*x + e
y = b0 + b1x1 + b2x2 + ... + bn*xn
What function from scikit-learn is used to perform Linear Regression?
model_selection.train_test_split()
preprocessing.StandardScaler()
linear_model.LinearRegression()
linear_model.LogisticRegression()
What does a residual represent in linear regression?
The difference between the actual and predicted values of the dependent variable.
The intercept of the regression line.
The predicted value of the dependent variable.
The slope of the regression line.
What does a correlation coefficient of 0 indicate?
A strong negative linear relationship
No linear relationship
A strong positive linear relationship
A perfect linear relationship
Why is normality of errors an important assumption in linear regression?
It ensures the linearity of the relationship between variables
It is necessary for the calculation of the regression coefficients
It guarantees the homoscedasticity of the errors
It validates the use of hypothesis testing for the model's coefficients
What distinguishes simple linear regression from multiple linear regression?
Simple linear regression has one independent variable, while multiple linear regression has two or more.
Simple linear regression analyzes categorical data, while multiple linear regression analyzes numerical data.
Simple linear regression uses a curved line, while multiple linear regression uses a straight line.
There is no difference; the terms are interchangeable.
Which of these methods can be used to address heteroscedasticity?
Removing outliers
All of the above
Transforming the dependent variable
Adding more independent variables
What type of visualization tool is commonly used to initially assess the relationship between two continuous variables in linear regression?
Scatter plot
Bar chart
Histogram
Pie chart
What is the purpose of splitting the dataset into training and testing sets in Linear Regression?
To visualize the relationship between variables.
To handle missing values in the dataset.
To evaluate the model's performance on unseen data.
To reduce the dimensionality of the data.
What does the 'fit_intercept' parameter in 'LinearRegression()' control?
Whether to normalize the data before fitting.
Whether to use gradient descent for optimization.
Whether to calculate the slope of the line.
Whether to calculate the intercept (bias) of the line.