import numpy as np
from scipy.stats import linregress
import matplotlib.pyplot as plt

# Generate some example data
x = np.array([1, 2, 3, 4, 5])
y = np.array([2.5, 3.5, 4.5, 5.5, 6.5])

# Perform linear regression
slope, intercept, r_value, p_value, std_err = linregress(x, y)

# Calculate R^2
r_squared = r_value**2

# Print the slope, intercept, and R^2
print("Slope:", slope)
print("Intercept:", intercept)
print("R^2:", r_squared)

# Plot the data and the linear fit
plt.scatter(x, y, label='Data')
plt.plot(x, slope * x + intercept, color='red', label='Linear Fit')
plt.xlabel('X')
plt.ylabel('Y')
plt.legend()
plt.show()