numpy as np

Given data points

x = np.array([-7, 2, 0, -3, -8, -1, -6, -5, 0, 1, -4, 3]) y = np.array([1, 9, 7, 5, -2, 2, -1, 0, 5, 7, -2, 7])

Calculate the line of best fit (linear regression)

slope, intercept = np.polyfit(x, y, 1)

Round the slope to the nearest tenths

round(slope, 1)The value of the slope $m$ for the line of best fit is $0.9$.

Would you like further details on how this was calculated or any additional questions? Here are some related questions you can explore:

1. How do you calculate the equation of the line of best fit using the formula for the slope and y-intercept?
2. What is the significance of the slope and intercept in the context of linear regression?
3. How do you interpret the R-squared value in the context of linear regression?
4. Can the line of best fit be used for prediction? If so, how?
5. How does the distribution of data points affect the line of best fit?

Tip: If you want a more precise answer for the slope, you can consider using a larger dataset or applying other regression methods like polynomial fitting for non-linear trends.