Performing linear regression in Python using Excel data

Linear regression in Python can be carried out with the help of libraries such as pandas, scikit-learn, statsmodels, and matplotlib. The following is a step-by-step code example:

1. First, import the necessary libraries:

   # Import necessary libraries
   import pandas as pd
   import numpy as np
   import matplotlib.pyplot as plt
   from sklearn.model_selection import train_test_split
   import statsmodels.api as sm
   from statsmodels.graphics.regressionplots import plot_regress_exog
   from statsmodels.graphics.gofplots import qqplot

2. Then, we create an Excel file with test data. Of course, in a real-life scenario, you would not need the mock data – you would skip this step and load the data from Excel (see the next step) after loading the necessary libraries:

   # Step 0: Generate sample data and save as Excel file
   np.random.seed(0)
   n_samples = 100
   X = np.random.rand(n_samples, 2)  # Two features
   y = 2 * X[:, 0] + 3 * X[:, 1] ...