numpy as np import pandas as pd from sklearn.linear_model import LinearRegression

Data

x = np.array([4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]).reshape(-1, 1) y = np.array([28.78, 24.2, 22.92, 22.44, 19.66, 16.68, 15.9, 10.82, 8.94, 8.16, 7.98, 2.7])

Linear regression model

model = LinearRegression() model.fit(x, y)

Coefficients

slope = model.coef_[0] intercept = model.intercept_

slope, interceptThe regression line for the given data is:

$y = -2.21x + 36.72$

Would you like details on how this regression line was calculated or any further analysis?

Relative Questions:

1. What does the slope of the regression line indicate in this context?
2. How can we interpret the y-intercept in this regression equation?
3. Could this regression line be used to predict future temperatures, and what are the limitations?
4. How well does the regression line fit the data? Would you like me to calculate $R^2$?
5. What happens to the temperature as the years progress based on this model?

Tip:

Always check the goodness-of-fit of a regression line to understand how well it represents the data.