Regression Model Validation
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High Quality Content by WIKIPEDIA articles! In statistics, model validation is possibly the most important step in the model building sequence. It is also one of the most overlooked. Often the validation of a model seems to consist of nothing more than quoting the R2 statistic from the fit (which measures the fraction of the total variability in the response that is accounted for by the model).Unfortunately, a high R2 (coefficient of determination) value does not guarantee that the model fits the data well. Use of a model that does not fit the data well cannot provide good answers to the under...
High Quality Content by WIKIPEDIA articles! In statistics, model validation is possibly the most important step in the model building sequence. It is also one of the most overlooked. Often the validation of a model seems to consist of nothing more than quoting the R2 statistic from the fit (which measures the fraction of the total variability in the response that is accounted for by the model).Unfortunately, a high R2 (coefficient of determination) value does not guarantee that the model fits the data well. Use of a model that does not fit the data well cannot provide good answers to the underlying engineering or scientific questions under investigation. However to increase the precision of the R2, some statisticians suggest that you should use the adjusted R2 to reflect both the number of independent variables in the model and sample size. This is only useful for multiple regression.
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