1. What is Residual Value?

The residual value is the difference between an observed value and its predicted value in statistical models. It represents the error or unexplained variation in the model's prediction.

2. How Does the Calculator Work?

The calculator uses the simple residual formula:

Where:

• Observed — The actual measured value
• Predicted — The value predicted by your model

Explanation: Positive residuals indicate the model underestimated the actual value, while negative residuals indicate overestimation.

3. Importance of Residual Calculation

Details: Residual analysis is crucial for assessing model fit, identifying patterns in prediction errors, and improving statistical models.

4. Using the Calculator

Tips: Enter both observed and predicted values in the same units. The calculator will output the residual in those same units.

5. Frequently Asked Questions (FAQ)

Q1: What does a residual of zero mean?
A: A zero residual means the model's prediction perfectly matched the observed value for that data point.

Q2: How are residuals used in regression analysis?
A: Residuals help diagnose problems with regression models, check assumptions, and identify outliers.

Q3: What's the difference between residual and error?
A: Error typically refers to population-level discrepancies, while residuals are sample-level differences.

Q4: Should residuals follow a particular distribution?
A: For linear regression, residuals should ideally be normally distributed with mean zero.

Q5: Can I calculate residuals for non-linear models?
A: Yes, residuals can be calculated for any predictive model using the same formula.