linear fit

scripts/linear_fit.py

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+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()