To determine the appropriateness of the linear model based on the residuals, we need to analyze the given residuals: 0.26, 0.93, 0.5489, and 0.659.
Residuals indicate the difference between the observed values and the predicted values from the regression model. A good model is typically indicated by residuals that are randomly distributed around zero with no specific pattern, rather than being consistently positive or negative.
In this case, all the residuals are positive, which suggests that the predicted values are lower than the observed values. However, having all positive residuals does not necessarily mean that the model is inappropriate. The magnitude of the residuals is also important; they are all below 1, indicating that the errors in the predictions are relatively small.
Among the options given, the most relevant conclusion is based on the fact that the residuals are all positive and below 1. Thus, the conclusion that Jes can make is:
The linear model accurately represents the data since all residuals are less than 1.
While the model's predictions may tend to underestimate the observed values, the fact that the residuals are small in magnitude suggests that overall, the linear model still describes the data reasonably well.