The equation \( \text{SSR} + \text{SSE} - \text{SST} = ? \) relates to the components of the total sum of squares (SST) in regression analysis.
In this context:
- SST (Total Sum of Squares) measures the total variability in the response variable.
- SSR (Regression Sum of Squares) measures the variability explained by the regression model.
- SSE (Error Sum of Squares) measures the variability that is not explained by the regression model (the error).
The fundamental relationship among these components is given by the equation:
\[ \text{SST} = \text{SSR} + \text{SSE} \]
So, if you rearrange this equation, you get:
\[ \text{SSR} + \text{SSE} - \text{SST} = 0 \]
Therefore:
\[ \text{SSR} + \text{SSE} - \text{SST} = 0 \]