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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. Sh...Question
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)
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 does not 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 does not accurately represent the data since the residual 9.3 means that the actual point is far from the estimate
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 does not 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 does not accurately represent the data since the residual 9.3 means that the actual point is far from the estimate
Answers
GPT-4o mini
Jade can conclude that "The linear model does not accurately represent the data since the residual 9.3 means that the actual point is far from the estimate."
This is because a large positive residual (like 9.3) indicates that the actual value is significantly higher than the predicted value, suggesting that the linear model may not fit the data well overall. In general, when examining residuals, large magnitudes (positive or negative) can signal poor model fit.
This is because a large positive residual (like 9.3) indicates that the actual value is significantly higher than the predicted value, suggesting that the linear model may not fit the data well overall. In general, when examining residuals, large magnitudes (positive or negative) can signal poor model fit.