Sales Prediction Model based on Multifactorial Linear Regression

Jiyan Liu

2022

Abstract

Motivated by the sales prediction of ABCtronics, this paper aims to provide integrated process for creating a proper multiple linear regression model and detailed analysis of according parameters. With multiple independent variables, the linear regression model exams the relationship between a collection of data-overall market demand, price per chip and economic condition to the single dependent variable sales volume of ABCtronics. The target is set to be future prediction of sales volume with three observable variables. All outputs are statistical results from Minitab which contain regression equation, model summary with R 2 value and analysis of variance stating the significance level of each variable. With detailed output, a refined multiple regression model is then developed to get rid of the overall market demand term. Pure regression equation brings only numerical understanding, i.e., visualization is the next focus. Based on comparison and contrast, it is verified the accurateness of the refined model. These results shed light on the proper use of multiple linear regression on sale prediction model.

Paper Citation

in Harvard Style

Liu J. (2022). Sales Prediction Model based on Multifactorial Linear Regression. In Proceedings of the International Conference on Big Data Economy and Digital Management - Volume 1: BDEDM, ISBN 978-989-758-593-7, pages 598-604. DOI: 10.5220/0011195100003440

in Bibtex Style

@conference{bdedm22,
author={Jiyan Liu},
title={Sales Prediction Model based on Multifactorial Linear Regression},
booktitle={Proceedings of the International Conference on Big Data Economy and Digital Management - Volume 1: BDEDM,},
year={2022},
pages={598-604},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011195100003440},
isbn={978-989-758-593-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Big Data Economy and Digital Management - Volume 1: BDEDM,
TI - Sales Prediction Model based on Multifactorial Linear Regression
SN - 978-989-758-593-7
AU - Liu J.
PY - 2022
SP - 598
EP - 604
DO - 10.5220/0011195100003440