线性回归模型在工业生产总值预测中应用VIP专享

南 昌 工 程 学 院毕 业 设 计 (论 文) 理学系 系〔院〕 信息与计算科学 专业论文题目 多元回归模型及其在工业生产总值预测中的应用讨论 学生姓名 班 级 学 号 指导老师 多元回归模型及其在工业生产总值预测中的应用讨论Multiple regression model and its application in industrial production prediction总计 论 文 26 页 表 格 7 个 插 图 2 副摘 要 经济指标预测是一项具有挑战性的讨论工作。本文利用多元回归模型讨论经济指标影响因素，并为资源优化配置提供一定参考意见。以中国各省制造业工业生产总值为讨论对象，依据背景知识选取七个自变量：朔料制品、水泥、玻璃、原煤、生铁、粗钢、钢筋、盘条，建立多元线性回归模型。再通过观测猎取 n 组观测数据，应用最小二乘法求出回归参数估量值。运用回归方程的显著性检验，回归系数的显著性检验，多重共线性检验，异方差检验等检验方法法删除不符合线性关系的自变量或得到更符合实际关系的多元线性模型。关键词：工业生产总值，多元回归模型，资源优化配置，经济预测。AbstractEconomic index prediction is a challenging research work. In this paper, using multivariate regression model of factors influencing economic index, and optimal allocation of resources to provide a certain reference. In China manufacturing industry gross industrial production as the research object, based on the background knowledge of selected seven variables: Schaumburg material products, cement, glass, coal, pig iron, crude steel, rebar, wire rod, established a multiple linear regression model. Through observation to obtain the N groups of observation data, the application of the least squares method to get regression parameter estimation. Application of significance test of regression equation the significance test of regression coefficients, the multicollinearity of inspection, testing for heteroscedasticity test method to delete not consistent with the linear relationship between variables or get more accord with real relationship in ...