pandas as pd import numpy as np from scipy.stats import linregress

Data extracted from the image

data = { "x": [2.3, 2.8, 3.1, 3.0, 3.5, 3.8], "y": [6.2, 5.7, 4.7, 3.2, 3.0, 2.8] }

Creating a DataFrame

df = pd.DataFrame(data)

Performing linear regression

slope, intercept, r_value, p_value, std_err = linregress(df['x'], df['y'])

Rounding slope and intercept to 2 decimal places

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

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

$y = -2.45x + 11.83$

Would you like a step-by-step explanation or have any questions?

Here are 5 related questions to expand understanding:

1. How is the slope of a line calculated using data points?
2. What does the slope (-2.45) signify in this context?
3. What role does the intercept (11.83) play in the equation?
4. How can you use the line of best fit to make predictions?
5. What does the correlation coefficient ($r$) indicate in this case?

Tip: Always visualize the data alongside the line of best fit to better understand its accuracy and the relationship between variables!