Answer: root mean square error.

## Explanation In simple linear regression, the **standard error of the estimate** (SEE) is also known as the **root mean square error** (RMSE). ### Key Concepts: 1. **Standard Error of the Estimate (SEE)**: Measures the standard deviation of the residuals (errors) in a regression model. It represents the average distance that the observed values fall from the regression line. 2. **Root Mean Square Error (RMSE)**: This is calculated as: \[ RMSE = \sqrt{\frac{\sum_{i=1}^{n}(y_i - \hat{y}_i)^2}{n-k-1}} \] where: - \(y_i\) = actual values - \(\hat{y}_i\) = predicted values - \(n\) = number of observations - \(k\) = number of independent variables (for simple linear regression, k=1) 3. **Why not the other options**: - **Mean Square Error (MSE)**: This is the average of the squared errors, not the standard error. MSE = SEE² - **Sum of Squares Error (SSE)**: This is the total of squared errors, not the standard error. SSE = Σ(y_i - ŷ_i)² ### Formula Relationship: \[ SEE = RMSE = \sqrt{MSE} = \sqrt{\frac{SSE}{n - 2}} \quad \text{(for simple linear regression)} \] Therefore, the correct answer is **C. root mean square error**.

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