 Analysis of influencing factors of grain yield based on multiple linear regressionVictor Chang and Qianwen Xu International Journal of Business and Systems Research, 2021, vol. 15, issue 3, 337-355 Abstract: 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. Keywords: grain yield; influencing factors; prediction; stepwise regression model; principal component regression. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbsre:v:15:y:2021:i:3:p:337-355 Access Statistics for this article More articles in International Journal of Business and Systems Research from Inderscience Enterprises Ltd |
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