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5. 基于线性回归模型对北京市PM2.5含量的统计分析

基于线性回归模型对北京市PM2.5含量的统计分析

来源 :科技创新与应用 | 被引量 : 0次 | 上传用户：yiyiweiwei

【摘 要】

：

为探究北京市PM2.5含量与其他指标之间的关系,文章基于北京市2020年一整年的AQI指数及其六项指标进行回归分析.首先,通过散点图初步判断PM2.5的指标与其他几项指标间的线性关系.然后,检验各自变量指标间是否存在相关性,即模型是否存在复共线性.接着对变量运用主成分回归,选取合适的主成分,来消除变量间复共线性对拟合线性回归模型的影响.之后对主成分回归得到的线性模型进行统计诊断,消除异常值点对模型的影响.最后,用逐步回归得到PM2.5与PM10、SO2、CO、NO2、O3最终的线性回归模型.

【作 者】

：

廖锡欧 

【机 构】

：

首都师范大学 数学科学学院,北京 100048

【出 处】

：

科技创新与应用

【发表日期】

：

2021年35期

【关键词】

：

PM2.5 相关分析 主成分回归 统计诊断 cook距离 

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论文部分内容阅读

为探究北京市PM2.5含量与其他指标之间的关系,文章基于北京市2020年一整年的AQI指数及其六项指标进行回归分析.首先,通过散点图初步判断PM2.5的指标与其他几项指标间的线性关系.然后,检验各自变量指标间是否存在相关性,即模型是否存在复共线性.接着对变量运用主成分回归,选取合适的主成分,来消除变量间复共线性对拟合线性回归模型的影响.之后对主成分回归得到的线性模型进行统计诊断,消除异常值点对模型的影响.最后,用逐步回归得到PM2.5与PM10、SO2、CO、NO2、O3最终的线性回归模型.

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