Analysis of influencing factors of grain yield based on multiple linear regression


Food security is a strategic issue affecting economic development and social stability and agriculture has always been at the forefront of national economic development. As a large agricultural country and a country with a large population, the production of grain is of great importance to China. Therefore, in order to ensure national food security and assist the food administrative department in making scientific and effective decisions, it is significant to study the law of variance in grain production and make accurate forecasting of its development trend. This paper constructs the stepwise regression model and principal component regression to analyse the influencing factors of grain yield respectively and compares these two models in terms of their accuracy in prediction. After conducting the two regressions, this paper concludes that the two models both explain the variance in grain yield ideally, but from the aspect of accuracy in prediction, the principal component regression is more effective than stepwise linear regression.

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Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences
Additional Information: Copyright © 2021 Inderscience Enterprises Ltd.
Uncontrolled Keywords: Grain yield,Influencing factors,Prediction,Principal component regression,Stepwise regression model,Management Information Systems,Business and International Management,Strategy and Management
Publication ISSN: 1751-2018
Last Modified: 22 Apr 2024 07:31
Date Deposited: 09 Jun 2022 14:37
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.ind ... BSR.2021.114934 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-02-23
Accepted Date: 2021-02-01
Authors: Chang, V. (ORCID Profile 0000-0002-8012-5852)
Xu, Q. (ORCID Profile 0000-0003-0360-7193)

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