Understanding SEE and R-Square: The Impact of Data Selection on Statistical Models
In this meta quiz, we'll test your understanding of the coefficient of determination (R-square) and related concepts. You might encounter various concepts combined in one sentence to test your understanding of the relationships between different indicators.
Let's examine the connection between sample data, Standard Error of Estimate (SEE), and R-square. For example, if you're conducting a regression with 100 sample data points, you might find data with very large or very small residuals. Deleting these data points will affect both the SEE and R-square.
There are two scenarios to consider:
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Deleting Small Residual Data:
- Small residuals indicate good data
- Deleting good data makes your model worse
- SEE becomes larger than before
- Prediction accuracy decreases
- R-square decreases due to reduced explanatory power
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Deleting Large Residual Data:
- Large residuals indicate bad data
- Removing bad data improves your model
- SEE becomes smaller
- Model's goodness of fit increases
- R-square increases
When solving these types of questions, focus on whether you're dealing with good or bad data rather than worrying about the residual perspective. Simply consider how adding or removing data points affects the SEE and R-square values based on data quality.