What function from scikit-learn is used to perform Linear Regression?
linear_model.LinearRegression()
preprocessing.StandardScaler()
linear_model.LogisticRegression()
model_selection.train_test_split()
What does the assumption of independence in linear regression refer to?
Independence between the observations
Independence between the errors and the dependent variable
Independence between the coefficients of the regression model
Independence between the independent and dependent variables
What does the linearity assumption in linear regression imply?
The relationship between the dependent and independent variables can be best represented by a straight line.
The data points are evenly distributed around the regression line.
The independent variables are unrelated to each other.
The dependent variable must have a normal distribution.
Which Python library is primarily used for numerical computing and provides support for arrays and matrices, essential for Linear Regression calculations?
pandas
scikit-learn
NumPy
matplotlib
Which of the following is NOT an assumption of linear regression?
Multicollinearity
Linearity
Normality of residuals
Homoscedasticity
Which assumption of linear regression ensures that the relationship between the independent and dependent variables is linear?
Normality of errors
Independence
What type of visualization tool is commonly used to initially assess the relationship between two continuous variables in linear regression?
Histogram
Scatter plot
Pie chart
Bar chart
Who is credited with developing the foundational principles of linear regression?
Marie Curie
Albert Einstein
Sir Francis Galton
Isaac Newton
What is the purpose of the coefficient of determination (R-squared) in linear regression?
To identify the presence of outliers in the data.
To determine the statistical significance of the model.
To measure the proportion of variation in the dependent variable explained by the independent variable(s).
To assess the linearity assumption of the model.
Which of the following is NOT a benefit of feature selection in linear regression?
Improved model interpretability
Reduced computational cost
Increased risk of overfitting
Potential for better generalization to new data