Linear regression is a statistical method for modeling the linear relationship between a dependent variable and one or more independent variables. In Python, this relationship can be modeled using the `'statsmodels`

‘ or `'sklearn`

‘ libraries.

Here is an example of how to perform linear regression in Python using the `statsmodels`

library:

# Import the required libraries import numpy as np import statsmodels.api as sm # Define the independent and dependent variables x = np.array([1, 2, 3, 4, 5]) y = np.array([2, 3, 4, 5, 6]) # Add a constant term to the independent variable x = sm.add_constant(x) # Create a linear regression model model = sm.OLS(y, x) # Fit the model to the data results = model.fit() # Print the model summary print(results.summary())

In this example, the independent variable is ‘x’ and the dependent variable is ‘y’. The ‘statsmodels’ library is used to add a constant term to the independent variable, create a linear regression model, and fit the model to the data. Finally, the model summary is printed, which provides information about the coefficients, p-values, and other statistical measures associated with the model.

Alternatively, you can use the ‘sklearn’ library to perform linear regression in Python. Here is an example of how to do this:

# Import the required libraries import numpy as np from sklearn.linear_model import LinearRegression # Define the independent and dependent variables x = np.array([1, 2, 3, 4, 5]).reshape(-1, 1) y = np.array([2, 3, 4, 5, 6]) # Create a linear regression model model = LinearRegression() # Fit the model to the data model.fit(x, y) # Print the coefficients and intercept print('Coefficients:', model.coef_) print('Intercept:', model.intercept_)

In this example, the `'sklearn'`

library is used to create a linear regression model and fit it to the data. The coefficients and intercept of the model are then printed.

Both of these examples demonstrate how to perform linear regression in Python using two different libraries. The `'statsmodels`

‘ library provides a more detailed summary of the model, while the ‘sklearn’ the library is simpler to use and allows you to easily access the coefficients and intercept of the model.