Research on Heilongjiang Rice Industry Based on Multiple Linear Regression Analysis

Abstract

Against the backdrop of a severe global food security situation, accurate prediction of crop yields is crucial for ensuring stable food supply and maintaining socio-economic stability. Heilongjiang Province is an important region for rice production in China, and its rice yield is of great significance to the stable supply of national grain. This study uses multiple linear regression and random forest regression models to conduct in-depth research and predictive analysis on rice production in various cities of Heilongjiang Province. The data is sourced from authoritative databases and field research, after data cleaning, which includes removing outliers, handling missing values, and correcting errors, and standardization, which normalizes data to a common scale (such as using z-score normalization or min-max scaling) to ensure consistency and comparability, models are constructed. The multiple linear regression model is based on the assumption of linear relationship, estimates parameters using the least squares method, and rigorously tests and evaluates them; The random forest regression model, as an optimization model, utilizes multiple decision trees for ensemble learning to improve prediction accuracy and reliability. Comparing the predicted values of the two models, it was found that they each have their own advantages, providing diverse and powerful support for predicting rice yield in Heilongjiang. This not only contributes to the scientific planning and sustainable development of the rice industry in Heilongjiang Province, but also has profound significance for ensuring national food security and promoting the modernization of agriculture.