A regression model summary had the results (R = .63, R2 = .40). This model explained 40% of the variance in a customs agent’s decision to search a traveller for drugs. How much variance in a customs agent’s decision to search a traveller for drugs was due to error?
Question
A regression model summary had the results (R = .63, R2 = .40). This model explained 40% of the variance in a customs agent’s decision to search a traveller for drugs. How much variance in a customs agent’s decision to search a traveller for drugs was due to error?
Solution
To determine the variance in a customs agent’s decision to search a traveller for drugs that was due to error, we need to consider the R-squared (R²) value provided in the regression model summary. The R² value represents the proportion of the variance in the dependent variable (in this case, the decision to search a traveller for drugs) that is explained by the independent variables in the model.
Given:
- R² = 0.40
This means that 40% of the variance in the decision to search a traveller for drugs is explained by the model. The remaining variance is due to error or other factors not included in the model.
To find the variance due to error, we subtract the R² value from 1 (or 100% if expressed as a percentage):
Variance due to error = 1 - R² = 1 - 0.40 = 0.60
Therefore, 60% of the variance in a customs agent’s decision to search a traveller for drugs was due to error.
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