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.
|Number of pages||19|
|Journal||International Journal of Business and Systems Research|
|Publication status||Published - 23 Feb 2021|
Bibliographical noteCopyright © 2021 Inderscience Enterprises Ltd.
- Grain yield
- Influencing factors
- Principal component regression
- Stepwise regression model