Final answer:
The standard deviation of the residual in regression analysis is more commonly referred to as the standard error of the estimate, which measures how scattered the residuals are around the regression line. The formula to calculate it is se = √(SSE/(n-2)), and the correct answer to the provided question is C. The standard error of the estimate.
Step-by-step explanation:
The question asks us to identify what se, the standard deviation of the residual, is more commonly referred to as. In the context of regression analysis, the standard deviation of the residual, denoted se, is a measure of how scattered the residuals are around the regression line. It's important to note that residuals are the differences between observed values and the values predicted by the regression model. The correct term we use for se is The standard error of the estimate, which helps us in assessing the precision of our prediction. This term encompasses not just the variability of the sample data but also the accuracy with which we can estimate the population parameter.
To calculate se, we use the formula se = √(SSE/(n-2)), where SSE is the sum of the squared errors (or residuals), and n is the total number of data points. The standard error of the estimate is a critical component in constructing confidence intervals and conducting hypothesis tests, and it varies from sample to sample due to sampling variability. This measure is usually preferred over the sample standard deviation as it takes into account the degrees of freedom (n-2) specifically for regression models.
Given the context and the options provided, the correct answer to the question is C. The standard error of the estimate.