Final answer:
The sum of squared error (SSE) is a measure used in statistics to quantify the difference between observed and predicted values. It is often used in regression analysis to assess the accuracy of the model's predictions.
Step-by-step explanation:
The sum of squared error (SSE) is a measure used in statistics to quantify the difference between observed and predicted values. It is often used in regression analysis to assess the accuracy of the model's predictions. To calculate SSE, you need to square the difference between each observed value and its corresponding predicted value, and then sum up all these squared differences.
In the given scenario, the sum of squared errors is 2,440. This means that the total difference between the observed values and the predicted values, when squared, adds up to 2,440.