Statistic regression and open data approach for identifying economic indicators that influence e-commerce

Abstract : — This paper presents a statistical approach to identify explanatory variables linearly related to e-commerce sales. The proposed methodology allows specifying a regression model in order to quantify the relevance between openly available data (economic and demographic) and national e-commerce sales. The proposed methodology consists in collecting data, preselecting input variables, performing regressions for choosing variables and models, testing and validating. The usefulness of the proposed approach is twofold: on the one hand, it allows identifying the variables that influence e-commerce sales with an accessible approach. And on the other hand, it can be used to model future sales from the input variables. Results show that e-commerce is linearly dependent on 11 economic and demographic indicators.
Document type :
Conference papers
Complete list of metadatas

Cited literature [14 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01790991
Contributor : Simon Tamayo Giraldo <>
Submitted on : Monday, May 14, 2018 - 11:30:09 AM
Last modification on : Wednesday, July 31, 2019 - 9:52:02 AM
Long-term archiving on : Tuesday, September 25, 2018 - 12:07:47 PM

File

ICSEI 2018 - Ecommerce regress...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01790991, version 1

Citation

Apollinaire Barme, Simon Tamayo, Gaudron Arthur. Statistic regression and open data approach for identifying economic indicators that influence e-commerce. 20th International Conference on Urban Transportation and City Logistics, May 2018, London, United Kingdom. ⟨hal-01790991⟩

Share

Metrics

Record views

150

Files downloads

539