To evaluate the appropriateness of the linear model based on the provided residuals (0.26, 0.93, 0.5489, and 0.659), we need to analyze the significance of these values.
Residuals indicate the difference between the observed values and the values predicted by the model. In general, the following points are important to consider:
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All the residuals being positive suggests that the model consistently underestimates the actual data points, which may indicate that a linear model is not the best fit if the pattern persists or if the residuals display a systematic trend.
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While the magnitude of residuals matters, having all residuals less than 1 alone doesn't determine if the model is appropriate—it's more about whether the residuals exhibit a random distribution around zero. If the residuals show a consistent pattern (like being entirely positive), this can imply that a linear model is not suitable.
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Simply having all residuals positive does not justify the accuracy of the model.
Thus, Jes can conclude that:
The linear model does not accurately represent the data since all residuals are positive.
This suggests that the linear model consistently underestimates the actual values instead of capturing the trend accurately.