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Jade was working on her statistics homework. One of the questions gave her a trend line and asked her to find the residuals. She found the following residuals 2.6, 9.3, −5.489 , and −6.59 . What conclusion can Jade make about the linear model based on the residuals?(1 point) Responses The linear model seems to accurately represent the data since half of the residuals are positive and half of the residuals are negative. The linear model seems to accurately represent the data since half of the residuals are positive and half of the residuals are negative. The linear model seems to accurately represent the data since the residual 2.6 means that the actual point is not far from the estimate. The linear model seems to accurately represent the data since the residual 2.6 means that the actual point is not far from the estimate. The linear model does not accurately represent the data since half of the residuals are positive and half of the residuals are negative. The linear model does not accurately represent the data since half of the residuals are positive and half of the residuals are negative. The linear model does not accurately represent the data since the residual 9.3 means that the actual point is far from the estimate. The linear model does not accurately represent the data since the residual 9.3 means that the actual point is far from the estimate.

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To assess the linear model based on the residuals provided (2.6, 9.3, -5.489, and -6.59), we need to consider what residuals indicate about the model's fit.

  1. A residual close to 0 suggests that the model's estimate is very close to the actual value, indicating a good fit.
  2. Positive residuals indicate that the model's estimate is lower than the actual value, while negative residuals indicate that the model's estimate is higher than the actual value.
  3. The magnitude of the residuals also matters; larger absolute values mean a larger difference between the predicted and actual values.

Based on the information given, the residual values include a significant positive value (9.3) and other relatively smaller values. The presence of a large residual like 9.3 suggests that there is at least one point where the model's estimate is quite far from the actual value, indicating a potential issue with the fit of the linear model.

Considering this, the most valid conclusion Jade can make is:

The linear model does not accurately represent the data since the residual 9.3 means that the actual point is far from the estimate.