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Multiple Regression Analysis of Factors Influencing Grain Yield

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DOI: 10.23977/cpcs.2023.070106 | Downloads: 24 | Views: 611


Jingjun Li 1


1 Social Statistics & Demography, University of Southampton, Southampton, SO17 1BJ, UK

Corresponding Author

Jingjun Li


Since ancient times, China has been a major agricultural country and is now the world's top food producer. With the second largest population in the world, it is of great relevance to investigate the factors influencing grain production. In this paper, we look at the main factors that affect grain yield. A simple multiple linear regression analysis was used to develop a model with fertiliser application, sown area, flooded area, farm machinery power and agricultural labour as independent variables and total annual grain production as the dependent variable. The resulting model fitted well and the observations were independent of each other. However, there was serious covariance between the variables, so we tested the model and concluded that the model satisfied the chi-squaredness, but there was more serious covariance between the variables, which affected the model building and could cause model distortion. So finally we build stepwise regression and ridge regression models respectively to eliminate the multicollinearity among the variables in order to optimise the model.


Annual grain yield; Regression analysis; Stepwise regression; Ridge regression


Jingjun Li, Multiple Regression Analysis of Factors Influencing Grain Yield. Computing, Performance and Communication Systems (2023) Vol. 7: 45-55. DOI:


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