import numpy as np

# Sample data
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 5, 7, 9])

# Perform linear regression and get covariance matrix
p, V = np.polyfit(x, y, 1, cov=True)

# p contains [slope, intercept]
slope = p[0]
intercept = p[1]

# V is the covariance matrix. The square root of the diagonal elements
# are the standard errors (uncertainties) of the coefficients.
slope_uncertainty = np.sqrt(V[0, 0])
intercept_uncertainty = np.sqrt(V[1, 1])

print(f"Slope: {slope:.4f} +/- {slope_uncertainty:.4f}")
print(f"Intercept: {intercept:.4f} +/- {intercept_uncertainty:.4f}")