High School

We appreciate your visit to You have been closely monitoring your bike s mileage recently Here is a table showing the amount paid for fuel in 3 and the corresponding. This page offers clear insights and highlights the essential aspects of the topic. Our goal is to provide a helpful and engaging learning experience. Explore the content and find the answers you need!

You have been closely monitoring your bike's mileage recently. Here is a table showing the amount paid for fuel (in 3) and the corresponding mileage (in Km):

| Amount Paid (in 3) | Distance (in Km) |
|-------------------|------------------|
| 80 | 20 |
| 50 | 60 |
| 100 | 50 |
| 15 | 16 |
| 25 | 14 |

Consider [tex]y[/tex] to be the amount paid and [tex]x[/tex] to be the corresponding mileage in Km. You have noted down the distance traveled each time when the fuel meter falls back to a fixed reference mark and predicted that the equation of the best fit line is [tex]y = 5x - 22[/tex].

What will be the value of the SSE (Sum of Squared Errors) with respect to the best fit line?

Answer :

The sum of squared errors (SSE) with respect to the best-fit line is 225864.

To calculate the sum of squared errors (SSE) with respect to the best-fit line, we need to find the difference between the predicted y-values and the actual y-values, square each difference, and sum them up.

Given the equation of the best-fit line: y = 5x - 22.

We have the following data:

Amount paid (y): 80, 50, 60, 100, 50

Distance (x): 20, 50, 60, 100, 50

Step 1: Calculate the predicted y-values (y_pred) using the equation of the best-fit line.

For each corresponding x-value, substitute it into the equation:

y_pred = 5x - 22.

Calculating the predicted y-values:

y_pred = 5(20) - 22 = 78

y_pred = 5(50) - 22 = 228

y_pred = 5(60) - 22 = 278

y_pred = 5(100) - 22 = 478

y_pred = 5(50) - 22 = 228

Step 2: Calculate the squared differences between the actual y-values and the predicted y-values.

For each corresponding data point, subtract the predicted y-value from the actual y-value, and square the difference.

Calculating the squared differences:

(80 - 78)² = 4

(50 - 228)² = 36100

(60 - 278)² = 42436

(100 - 478)² = 110224

(50 - 228)² = 36100

Step 3: Sum up the squared differences.

SSE = (4) + (36100) + (42436) + (110224) + (36100) = 225864.

Learn more about the sum of squared errors at

https://brainly.com/question/29493970

#SPJ4

Thanks for taking the time to read You have been closely monitoring your bike s mileage recently Here is a table showing the amount paid for fuel in 3 and the corresponding. We hope the insights shared have been valuable and enhanced your understanding of the topic. Don�t hesitate to browse our website for more informative and engaging content!

Rewritten by : Barada