Given the set of data points \( \{ (2,5), (4,10), (6,15), (8,20) \} \), find the equation of the line of best fit. Then, factor out the greatest common factor (GCF) from each term in the equation.

Step 1 ：1. Use the formula for the slope of the line of best fit, which is \( m = \frac{n(\Sigma xy) - (\Sigma x)(\Sigma y)}{n(\Sigma x^2) - (\Sigma x)^2} \). Plug in the given data points to find the slope.

Step 2 ：2. The sum of the x-values (\( \Sigma x \)) is \(2+4+6+8=20\). The sum of the y-values (\( \Sigma y \)) is \(5+10+15+20=50\). The sum of the product of the x and y values (\( \Sigma xy \)) is \(2*5+4*10+6*15+8*20=260\). The sum of the square of the x-values (\( \Sigma x^2 \)) is \(2^2+4^2+6^2+8^2=120\). Plug these numbers into the formula to find the slope.

Step 3 ：3. The slope \( m \) equals \( \frac{4(260) - 20*50}{4*120 - 20^2} = \frac{1040 - 1000}{480 - 400} = \frac{40}{80} = 0.5 \).

Step 4 ：4. Use the formula for the y-intercept of the line of best fit, which is \( b = \frac{\Sigma y - m\Sigma x}{n} \). Plug in the numbers we have calculated to find the y-intercept.

Step 5 ：5. The y-intercept \( b \) equals \( \frac{50 - 0.5*20}{4} = \frac{40}{4} = 10 \).

Step 6 ：6. So, the equation of the line of best fit is \( y = 0.5x + 10 \).

Step 7 ：7. Now factor out the greatest common factor (GCF) from each term in the equation. The GCF of 0.5 and 10 is 0.5, so the equation becomes \( y = 0.5(x + 20) \).