After determining the best fit line, there are a few critical questions that you need to answer, such as:

- How well does the best fit line represent the scatter plot?

- How well does the best fit line predict the new data?

Are the questions above answered well by the RSS? Attempt these two questions on your own before getting the answers from the professor in the following lecture.

You can play with the interactive graphic below and look at how the position of the regression line and the values of the RSS and the TSS change with a change in the values of β₀ and β₁.

Let’s go back to our Excel demonstration and see how you can find out the TSS and then the R² for the same example for which we had performed regression.

You can download the Excel sheet used in the demonstration for your reference.

**Comprehension**

The plot below represents a scatter plot of two variables X and Y, with the Y variable being dependent on X. Let’s assume that the line with the equation Y = X/2 + 3 plotted in the graph represents the best fit line. This is the same line that you found the equation of earlier.

You can find the value of the residual for each point, e.g., for x = 2, the residual would be 5 – 4 = 1.

Answer the following questions in order to consolidate your learning about RSS and R².

Apart from R², there is one more concept named RSE (residual squared error), which is linked to RSS. Let’s see what that is.