The best regression equation that models the data is:
y = 0.83x + 51.9
How to solve
To find the values of m and b, we can use the least squares method, which minimizes the sum of the squared errors between the predicted temperatures and the actual temperatures.
Using this method, we can calculate the slope (m) and y-intercept (b) of the regression line:
m ≈ 0.83
b ≈ 51.9
Therefore, the best regression equation that models the data is:
y = 0.83x + 51.9
This equation suggests that the average monthly temperature increases by approximately 0.83 degrees Fahrenheit for each month.