Recalculating in big data

The available memory on a desktop or laptop computer can easily be exceeded by large datasets when performing analytics or training machine learning algorithms. In this paper we propose simple methods to recalculate and update the value of descriptive statistics (averages, variance, coefficients of skewness and kurtosis) after rescaling, adding or excluding some data. The coefficient of determination from regression analysis is also recalculated after deleting each sample observation. The objective is to avoid including all the observations (especially in big datasets) when those statistics are recalculated. For this, only its previous value updated by the changed observations is needed, saving time in the respective calculation.