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1、 数据分析方法 实验报告专业班级 姓名 组别 同组实验人员信息与计算科学 01实验日期 2014-11-14 周五 5-8 节 指导老师 刘劲光 评分实验名称 数据的回归分析实验目的 熟悉变量间的回归模型并对变量进行预测实验步骤：实验步骤：一、 用 Excel 软件编写实验数据，并保存二、 安装 SPSS 并导入 Excel 保存的数据三、 按照题目要求，计算相应的数据，和绘制相应的图形四、 运行结果如下：Total Variance ExplainedInitial Eigenvalues Extraction Sums of Squared LoadingsComponent Total
2、% of Variance Cumulative % Total % of Variance Cumulative %1 2.421 80.687 80.687 2.421 80.687 80.6872 .534 17.809 98.4963 .045 1.504 100.000Extraction Method: Principal Component Analysis.Component MatrixaComponent1x1 .775x2 .943x3 .964Extraction Method: Principal Component Analysis.a. 1 components
3、extracted.Total Variance ExplainedInitial Eigenvaluesa Extraction Sums of Squared LoadingsComponent Total % of Variance Cumulative % Total % of Variance Cumulative %1 110.004 80.356 80.356 110.004 80.356 80.3562 25.324 18.499 98.855Raw3 1.568 1.145 100.0001 110.004 80.356 80.356 2.406 80.204 80.2042
4、 25.324 18.499 98.855Rescaled3 1.568 1.145 100.000Extraction Method: Principal Component Analysis.a. When analyzing a covariance matrix, the initial eigenvalues are the same across the raw and rescaled solution.CommunalitiesRaw RescaledInitial Extraction Initial Extractionx1 51.745 51.745 1.000 1.00
5、0x2 23.456 23.456 1.000 1.000x3 61.696 61.696 1.000 1.000Extraction Method: Principal Component Analysis.Component MatrixaRaw RescaledComponent Component1 2 3 1 2 3x1 5.865 -4.165 .060 .815 -.579 .008x2 4.419 1.678 1.056 .912 .347 .218x3 7.489 2.272 -.670 .953 .289 -.085Extraction Method: Principal
6、Component Analysis.a. 3 components extracted.Total Variance ExplainedInitial Eigenvaluesa Extraction Sums of Squared LoadingsComponent Total % of Variance Cumulative % Total % of Variance Cumulative %Raw 1 110.004 80.356 80.356 110.004 80.356 80.3562 25.324 18.499 98.855 25.324 18.499 98.8553 1.568
7、1.145 100.000 1.568 1.145 100.0001 110.004 80.356 80.356 2.406 80.204 80.2042 25.324 18.499 98.855 .539 17.966 98.170Rescaled3 1.568 1.145 100.000 .055 1.830 100.000Extraction Method: Principal Component Analysis.a. When analyzing a covariance matrix, the initial eigenvalues are the same across the
8、raw and rescaled solution.Component MatrixaRaw RescaledComponent Component1 2 3 1 2 3x1 5.865 -4.165 .060 .815 -.579 .008x2 4.419 1.678 1.056 .912 .347 .218x3 7.489 2.272 -.670 .953 .289 -.085Extraction Method: Principal Component Analysis.a. 3 components extracted.五、结果分析：为获得较精确的预测，自变量的取值不应过分偏离自变量观测数据的均值。指导教师评语：签字：年 月 日
