Asked by Justin Brady on May 31, 2024
Verified
SSE can never be
A) larger than SST.
B) smaller than SST.
C) equal to one.
D) equal to zero.
SSE
The sum of squared errors between observed and predicted values in statistical models, indicating the model's accuracy.
SST
Total Sum of Squares, a measure of the total variation in a dataset.
- Establish and clarify the purpose of the coefficient of determination in regression analysis contexts.
Verified Answer
ZK
Zybrea KnightJun 02, 2024
Final Answer :
A
Explanation :
SSE (Sum of Squares Error) represents the variation in the data that is not explained by the model. SST (Total Sum of Squares) represents the total variation in the data. Therefore, SSE cannot be larger than SST.
Learning Objectives
- Establish and clarify the purpose of the coefficient of determination in regression analysis contexts.
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