ट्रेंड के आसपास का वैरिएशन

# Store x_data and y_data, as times and distances, in df, and use ols() to fit a model to it.
df = pd.DataFrame(dict(____=x_data, ____=y_data))
model_fit = ols(____="distances ~ times", data=____).____()

# Extact the model parameters and their uncertainties
a0 = model_fit.____['Intercept']
e0 = model_fit.____['Intercept']
a1 = model_fit.____['times']
e1 = model_fit.____['times']

# Print the results with more meaningful names
print('Estimate    of the intercept = {:0.2f}'.format(____))
print('Uncertainty of the intercept = {:0.2f}'.format(____))
print('Estimate    of the slope = {:0.2f}'.format(____))
print('Uncertainty of the slope = {:0.2f}'.format(____))