What term is used for the difference between the observed value of the dependent variable and the predicted value?

The term used for the difference between the observed value of the dependent variable and the predicted value is "Residual." In the context of regression analysis, the residual represents the error in prediction; it quantifies how far off the model's predictions are from the actual observed values. This concept is fundamental in assessing the goodness-of-fit of a model, as smaller residuals indicate better accuracy in the model's predictions.

In statistical calculations, each residual is computed by subtracting the predicted value from the observed value. This results in a positive or negative number that shows whether the prediction underestimated or overestimated the actual value. Analyzing the residuals allows researchers to understand the discrepancies and make necessary adjustments to improve the model.

The other terms listed may refer to related concepts but do not specifically denote the difference in question. Variance pertains to the measure of how much values in a dataset differ from the mean value, error generally refers to inaccuracies in models or predictions in a broader sense, and deviation typically describes the distance of a value from a central tendency, which does not capture the specific context of the difference between observed and predicted values.