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The least amount of time is taken to make and implement
Predicted Values
Predicted values are the outcomes forecasted by a statistical model or algorithm based on existing data inputs.
Heteroscedasticity
A condition in regression analysis where the variance of the residuals or errors is not constant across all levels of an independent variable, violating one of the key assumptions of linear regression.
Errors
Differences between observed values and the values predicted by a model.
Standardized Residuals
Residuals that have been adjusted to have a mean of zero and a standard deviation of one, useful for comparing residuals across different datasets.
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