关键词: 采煤工作面, 多元统计分析软件（SPSS）, 多重共线性, 主成分分析, 瓦斯涌出量

Abstract: In order to predict the gas emission in the mine coal face effectively, combining the various factors which affect the gas emission, for the serious multicollinearity among factors causing the amplification of calculating error, it uses stepwise regression algorithm to optimize. Through SPSS software, it integrates multivariate principal component regression analysis algorithm to get the main factors which affect coal face gas emission, and establishes PCR-SPA predictive model. It compares the forecasting performance of the model with multiple linear regression prediction, gray correlation analysis prediction, BP neural network prediction results. The results show that：the model selects three main components variables, effectively reducing the amount of computation, with a high prediction accuracy, which can predict the mine face gas emission effectively.

Key words: coal working face, Statistical Package for Social Science（SPSS）, multicollinearity, principal component analysis, gas emission quantity