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Forecasting Case Analysis

  • 1 dependent variable (nominal), 1+ independent variable(s) (interval or ratio)

When selecting the model for the analysis, an important consideration is model fitting.  Adding independent variables to a linear regression model will always increase the explained variance of the model (typically expressed as R²).  However, overfitting can occur by adding too many variables to the model, which reduces model generalizability.  Occam’s razor describes the problem extremely well – a simple model is usually preferable to a more complex model.  Statistically, if a model includes a large number of variables, some of the variables will be statistically significant due to chance alone.