Open AccessDeveloping Statistical Modellings to Investigate the Internal Drivers for the Trend of Output Values in the Manufacturing Industry: Evidence from Chinese Enterprises
Author(s) -
Yuzhou Zhang,
Guang Gao,
Lidan Shou,
Dun Wu,
Guangping Fang,
Hua Ping Sun
Publication year - 2021
Publication title -
proceedings of business and economic studies
Language(s) - English
Resource type - Journals
eISSN - 2209-265X
pISSN - 2209-2641
DOI - 10.26689/pbes.v4i5.2494
Subject(s) - productivity , econometrics , pillar , manufacturing , order (exchange) , measure (data warehouse) , statistical model , generalized linear model , economics , operations management , computer science , business , engineering , statistics , marketing , mathematics , data mining , structural engineering , finance , macroeconomics
The manufacturing industry is an important pillar of the national economy. It is of vital importance to develop statistical modellings in order to quantify the relationship between potential internal drivers and the trend of output values in the manufacturing industry. However, only a few statistical modellings have been established to investigate such associations. This study developed the correlation coefficient model and generalized linear model (GLM) to measure the single and interactive effects of the internal drivers on the changes of the output values. For the GLM, different predictive variables were developed to fit into the dataset, and the performance of the models were compared using fitness parameters. Furthermore, an industry survey dataset for 1,180 manufacturing enterprises in 2020 was used to validate the models. The use of the GLM combining land area, number of employees, scientific research input, and labor productivity may have a great potential to bolster capacity in monitoring and predicting the trend of output values in the manufacture industry.