numpy as np from scipy.stats import linregress

Data points

x = np.array([31, 46, 24, 50, 28, 33]) y = np.array([81, 54, 102, 43, 93, 67])

Perform linear regression

slope, intercept, r_value, p_value, std_err = linregress(x, y)

Round slope and intercept to the nearest hundredth

slope = round(slope, 2) intercept = round(intercept, 2)

slope, interceptThe equation of the line of best fit for the data is:

y = -2.13x + 148.49

Would you like me to calculate or explain anything further about this?

Here are five related questions to consider:

1. How can we interpret the slope of this line in context?
2. What is the meaning of the y-intercept in this equation?
3. How well does this line fit the data (e.g., using R-squared)?
4. Can we use this equation to predict values of $y$ for specific $x$?
5. How would the line of best fit change if we added more data points?

Tip: Always evaluate the goodness of fit (e.g., $R^2$) to understand how well the line represents the data.