Calculate the sum of squared errors (SSE) from the following data:Q (actual)81012Q (predicted)8813
Question
Calculate the sum of squared errors (SSE) from the following data:
- Q (actual) 8 10 12
- Q (predicted) 8 8 13
Solution
Sure, to calculate the sum of squared errors (SSE), you first need to find the difference between the actual and predicted values for each data point, square these differences, and then sum them up.
Here's how you do it:
-
Calculate the difference between actual and predicted values: For the first data point: 8 - 8 = 0 For the second data point: 10 - 8 = 2 For the third data point: 12 - 13 = -1
-
Square these differences: For the first data point: 0^2 = 0 For the second data point: 2^2 = 4 For the third data point: (-1)^2 = 1
-
Sum up these squared differences: SSE = 0 + 4 + 1 = 5
So, the sum of squared errors (SSE) for your data is 5.
Similar Questions
What does SSE (Sum of Squared Errors) represent in the context of regression analysis?
If the standard error of estimate = 20 and n = 8, then the sum of squares for error, SSE, is 2400.Group of answer choicesTrueFalse
What would be the coefficient of determination if the total sum of squares (SST) is 23.29 and the sum of squares due to regression (SSR) is 10.03?
Which of the following is called Standard Error? T-statistic squared Square root of SSE Square root of SST Square root of MSE
In an analysis of variance problem if SST (or TSS) = 120 and SSTR (or BSS)= 80, then SSE (or WSS) isGroup of answer choices2004080120
Upgrade your grade with Knowee
Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.