Sum of Squared Errors (SSE) for Best-Fit Line

Sum of Squared Errors (SSE) for Best-Fit Line


IITM Quiz 1


1๏ธโƒฃ Sum of Squared Errors (SSE) for Best-Fit Line

(From Image 1)

Question: Given the table of amount paid and distance (in km), and the best-fit line $ y = 4x + 1 $, what is the value of SSE?

Step-by-Step Explanation: Calculating SSE for the Best-Fit Line ๐Ÿ“Š

Let’s break down how to find the Sum of Squared Errors (SSE) for the given data and the best-fit line $ y = 4x + 1 $, step by step!

1๏ธโƒฃ List the Data Points

Amount Paid ($ y $)Distance ($ x $)
8020
6015
6016
10025
5814

2๏ธโƒฃ Calculate Predicted Values ($ y_{pred} $)

Use the best-fit line equation $ y = 4x + 1 $:

  • For $ x = 20 $: $ y_{pred} = 4 \times 20 + 1 = 81 $
  • For $ x = 15 $: $ y_{pred} = 4 \times 15 + 1 = 61 $
  • For $ x = 16 $: $ y_{pred} = 4 \times 16 + 1 = 65 $
  • For $ x = 25 $: $ y_{pred} = 4 \times 25 + 1 = 101 $
  • For $ x = 14 $: $ y_{pred} = 4 \times 14 + 1 = 57 $

3๏ธโƒฃ Find the Error for Each Point

Error is the difference between the actual and predicted value:

$$ \text{Error} = y_{\text{actual}} - y_{\text{pred}} $$
$ y_{actual} $$ y_{pred} $Error
8081$ -1 $
6061$ -1 $
6065$ -5 $
100101$ -1 $
5857$ +1 $

4๏ธโƒฃ Square Each Error

$$ \text{Squared Error} = (\text{Error})^2 $$
ErrorSquared Error
-11
-11
-525
-11
+11

5๏ธโƒฃ Sum All Squared Errors (SSE)

$$ \text{SSE} = 1 + 1 + 25 + 1 + 1 = \boxed{29} $$

๐Ÿ Final Answer

The Sum of Squared Errors (SSE) for the given data and the best-fit line $ y = 4x + 1 $ is:

$$ \boxed{29} $$

What Does SSE Mean?

  • SSE measures how well the line fits the data.
  • A smaller SSE means the line fits the data points more closely.
  • Here, each squared error shows how far the actual value is from the predicted value, and their sum gives the total error for all points.

Thatโ€™s how you find SSE, step by step, with formulas and calculations! ๐Ÿš€