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In regression analysis, what does the error of a model represent?

The total market value of the data set

The difference between actual data points and the predicted values

The difference between the data points and the trend line generated by the algorithm

In regression analysis, the error of a model primarily represents the difference between actual data points and the predicted values generated by the model. This is crucial because it quantifies the model's accuracy in predicting outcomes based on input features.

The error is formally known as the "residual," which is calculated for each data point as the actual value minus the predicted value. This residual indicates how far off the model's predictions are from the actual results. By assessing the size of these errors, one can gauge the model's performance and make adjustments to improve its accuracy.

Understanding the nature of errors (residuals) also helps in diagnosing whether the model may be systematically underpredicting or overpredicting certain values and indicates areas where the model could potentially be enhanced.

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The average of all prediction errors

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