1. What is Sum of Squared Errors (SSE)?

The Sum of Squared Errors (SSE) is a measure of the discrepancy between observed data and the values predicted by a model. It's a key metric in regression analysis and statistical modeling.

2. How Does the Calculator Work?

The calculator uses the SSE formula:

Where:

• \( y_i \) — Observed values
• \( \hat{y}_i \) — Predicted values
• \( n \) — Number of observations

Explanation: For each data point, the calculator computes the difference between observed and predicted values, squares this difference, and sums all these squared differences.

3. Importance of SSE Calculation

Details: SSE is fundamental in regression analysis. Lower SSE values indicate better model fit. It's used to compare models and is the basis for calculating other statistics like MSE and R-squared.

4. Using the Calculator

Tips: Enter matching sets of observed and predicted values. Values can be separated by commas or spaces. The calculator will pair them in order and compute the SSE.

5. Frequently Asked Questions (FAQ)

Q1: What's the difference between SSE and MSE?
A: MSE (Mean Squared Error) is SSE divided by the number of observations, making it a normalized version of SSE.

Q2: Can SSE be negative?
A: No, since all errors are squared, SSE is always ≥0. A value of 0 indicates perfect prediction.

Q3: What units does SSE have?
A: SSE has units squared (e.g., if y is in meters, SSE is in meters²).

Q4: How do I interpret SSE values?
A: SSE should be interpreted relative to the scale of your data. Lower is better, but there's no universal threshold.

Q5: What if my observed and predicted sets have different lengths?
A: The calculator will only process pairs where both observed and predicted values exist.