Statistical analysis of solid waste composition data: Arithmetic mean, standard deviation and correlation coefficients

• Published in: Waste management (Elmsford) Vol. 69; pp. 13 - 23
• Main Authors: Edjabou, Maklawe Essonanawe, Martín-Fernández, Josep Antoni, Scheutz, Charlotte, Astrup, Thomas Fruergaard
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.11.2017
• Subjects: Animal Feed; Compositional data analysis; Data Collection; Isometric log ratio; Plastics; Product Packaging; Solid Waste - analysis; Solid Waste - classification; Solid Waste - statistics & numerical data; Variation array; Vegetables; Waste composition; Waste composition; Variation array; Isometric log ratio; Compositional data analysis
• ISSN: 0956-053X; 1879-2456
• DOI: 10.1016/j.wasman.2017.08.036

•Data for waste fraction compositions represent closed datasets.•Classical statistics are ill-suited to data for waste fraction compositions.•Isometric log-ratio coordinates are appropriate transformation prior to statistical analysis. Data for fractional solid waste composition provide relative magnitudes of individual waste fractions, the percentages of which always sum to 100, thereby connecting them intrinsically. Due to this sum constraint, waste composition data represent closed data, and their interpretation and analysis require statistical methods, other than classical statistics that are suitable only for non-constrained data such as absolute values. However, the closed characteristics of waste composition data are often ignored when analysed. The results of this study showed, for example, that unavoidable animal-derived food waste amounted to 2.21±3.12% with a confidence interval of (−4.03; 8.45), which highlights the problem of the biased negative proportions. A Pearson’s correlation test, applied to waste fraction generation (kg mass), indicated a positive correlation between avoidable vegetable food waste and plastic packaging. However, correlation tests applied to waste fraction compositions (percentage values) showed a negative association in this regard, thus demonstrating that statistical analyses applied to compositional waste fraction data, without addressing the closed characteristics of these data, have the potential to generate spurious or misleading results. Therefore, ¨compositional data should be transformed adequately prior to any statistical analysis, such as computing mean, standard deviation and correlation coefficients.