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Residual Definition

The difference between the measured and predicted value.

A residual is the difference between an observed value and the value predicted by a statistical model, such as regression. It represents the unexplained or error portion of the prediction.

Who relies on residuals in market research?

Data analysts, statisticians, quantitative researchers and modelers rely on residuals to assess the accuracy and fit of predictive models used in market research.

What are key aspects of residuals in market research?

• Calculated as actual minus predicted value.
• Used to evaluate model performance.
• Should be randomly distributed if the model is appropriate.
• Patterns in residuals may indicate model issues.
• Can be positive or negative.

Why are residuals important in market research?

Residuals help researchers identify how well a model captures real-world data. Analyzing residuals reveals model accuracy, highlights data irregularities and supports model refinement.

How do market researchers examine residuals?

Researchers examine residuals to validate regression models, check for bias, detect outliers and ensure assumptions like linearity and homoscedasticity are met. This ensures reliable forecasts and decision-making.