To assess the appropriateness of the linear model based on the residuals provided (0.26, 0.93, 0.5489, and 0.659), it's important to analyze what the residuals represent. Residuals are the differences between observed values and the values predicted by the model. Smaller residuals indicate a better fit of the model to the data.
- All residuals are less than 1: This suggests that the predictions made by the linear model are relatively close to the actual data points, indicating a reasonable fit.
- All residuals are positive: This means that the model consistently underestimates the actual values. While this could suggest a trend in the model's predictions, it doesn't necessarily mean the model is inaccurate; the important factor is the magnitude of residuals.
- All data points are less than 1: This statement does not apply to the evaluation of the linear model based on the residuals since it is not relevant to the relationship between residuals and the model's appropriateness.
Given the information about the residuals being less than 1 and positive, the best conclusion Jes can make is:
The linear model accurately represents the data since all residuals are less than 1.
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