linear_regression_monomial.py

from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np

data_time = [1.08E-01, 1.25E-01, 1.41E-01, 1.58E-01, 1.75E-01, 1.91E-01]
data_velocity_y = [-2.78E+00, -3.00E+00, -3.27E+00, -3.56E+00, -3.71E+00, -3.88E+00]

regression = LinearRegression()

regression.fit(np.array(data_time).reshape(-1, 1), np.array(data_velocity_y).reshape(-1, 1))

coef = regression.coef_[0][0]
intercept = regression.intercept_

print(coef)

plt.title("vy")
plt.plot([data_time[0], data_time[5]], [data_time[0]*coef + intercept, data_time[5]*coef + intercept])

plt.scatter(data_time, data_velocity_y)
plt.xlabel("t")
plt.ylabel("vy")
plt.show()