What does SSE (Sum of Squared Errors) represent in the context of regression analysis?

SSE (Sum of Squared Errors) represents the sum of the squared differences between the observed values and the predicted values in regression analysis. It is a measure of the overall deviation or error between the actual data points and the regression line.

To calculate SSE, follow these steps:

1. Start with a set of observed data points and their corresponding predicted values from the regression model.
2. Calculate the difference between each observed value and its corresponding predicted value.
3. Square each of these differences to eliminate negative values and emphasize larger errors.
4. Sum up all the squared differences to obtain the SSE.

SSE is an important metric in regression analysis as it quantifies the accuracy of the regression model. A lower SSE indicates a better fit of the regression line to the data, meaning that the model is able to explain a larger proportion of the variation in the dependent variable. Conversely, a higher SSE suggests that the model has a poorer fit and is not able to capture the relationship between the independent and dependent variables effectively.