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1、数据分析验证性实验报告一、 题目 1、1991 年我国30个省、区、市城镇居民月平均消费八个指标（单位均为元/人）X1: 人均粮食支出 X2: 人均副食支出X3: 人均烟茶支出 X4: 人均其它副食支出X5: 人均衣着商品支出 X6: 人均日用品支出X7: 人均燃料支出 X8: 人均非商品支出省区市X1X2X3X4X5X6X7X8山西8.3523.537.518.6217.4210.001.0411.21内蒙古9.2523.756.619.1917.7710.481.7210.51吉林8.1930.504.729.7816.287.602.5210.32黑龙江7.7329.205.429.43
2、19.298.492.5210.00河南9.4227.938.208.1416.179.421.559.76甘肃9.1627.989.019.3215.999.101.8211.35青海10.0628.6410.5210.0516.188.391.9610.81河北9.0928.127.409.6217.2611.122.4912.56陕西9.4128.205.7710.8016.3611.561.5312.17宁夏8.7028.127.2110.5319.4513.301.6611.96新疆6.9329.854.549.4916.6210.651.8813.61湖北8.6736.057.31
3、7.7516.6711.682.8312.88云南9.9837.697.018.9416.1511.080.8311.67湖南6.7738.696.018.8214.7911.441.7413.23安徽8.1437.759.618.4913.159.761.2811.28贵州7.6735.718.048.3115.137.761.4113.25辽宁7.9039.778.4912.9419.2711.052.0413.29四川7.1840.917.328.9417.6012.751.1414.08山东8.8233.707.5910.9818.8214.731.7810.10江西6.2535.02
4、4.726.2810.037.151.9310.39福建10.6052.417.709.9812.5311.702.3114.69广西7.2752.653.849.1613.0315.261.9814.57海南13.44555.855.507.459.559.522.2116.30天津10.8544.687.32145117.1312.081.2611.57江苏7.2145.797.6610.3616.5612.862.2511.69浙江7.6850.3711.3513.3019.2514.592.7514.87北京7.7848.448.0020.5122.1215.731.1516.61西藏
5、7.9439.6520.9720.8222.5212.411.757.90上海8.2864.348.0022.2220.0615.120.7222.89广东12.4776.395.5211.2414.5222.005.4625.50设前20个省份为第1类G1，21-27号省份（即福建，北京）为第2类G2，最后三个省份（西藏、上海、广东）待判。试判别西藏、上海、广东各属哪一类，并计算判别率的回代估计。80二、程序1.录入数据2. 按“Analyze Classify Discriminant ”顺序，打开Discriminant Analysis主对话框，选择“类别”为 Grouping Var
6、iable（分组变量），定义“类别”的区域，选择x1、x2、x3、x4、x5、x6、x7、x8为Indepent Variable（解释变量）。3. 点击Statistics按钮，进入Statistics对话框，在Descriptives栏选择Mean（对各组的各个变量作均值和标准差的描述；在Function Coefficients栏（判别函数的系数）选择Fishers(Fisher线性判别函数)和Unstandardized（判别方程的非标准化系数）非标准化函数；在Matrices栏选Within-groups correlation（组内相关矩阵）,Within-groups covar
7、iance(组内协方差矩阵)，Separate-groups covariance(组间协方差矩阵) ，Total covariance(总协方差矩阵)。4. 点击Classification按钮，进入Classification对话框。在Prior Probabilities栏选择All groups equal；在display栏选择casewise results（每个个体的结果），Summerry table（综合表）5. 点击Save按钮，保存选项中可以选择预测的分类、判别得分以及所属类别的概率。三、 运行结果表1协方差矩阵a类别x1x2x3x4x5x6x7x81x11.142-2.
8、486.886.432.809.328-.049-.512x2-2.48628.166.604-.289-2.9891.861-.3533.573x3.886.6042.639.407.339.204-.208-.027x4.432-.289.4071.9082.1671.204.069.277x5.809-2.989.3392.1674.8622.371.167.260x6.3281.861.2041.2042.3713.920-.176.894x7-.049-.353-.208.069.167-.176.275-.073x8-.5123.573-.027.277.260.894-.0731
9、.8202x15.7884.060-1.377-3.903-6.551-4.759-.047.843x24.06015.941-3.326-9.715-11.772-2.6181.0695.911x3-1.377-3.3265.3974.6406.3731.030.389-.242x4-3.903-9.7154.64019.28617.2255.855-1.6721.189x5-6.551-11.7726.37317.22518.5996.866-.976-.593x6-4.759-2.6181.0305.8556.8664.946-.275.535x7-.0471.069.389-1.672
10、-.976-.275.339.062x8.8435.911-.2421.189-.593.535.0624.024合计x12.3242.272.362-.083-1.058-.380-.011.277x22.27288.673.3487.799-7.70110.541.71913.319x3.362.3483.1811.4751.689.489-.055.022x4-.0837.7991.4757.4765.1133.789-.2191.964x5-1.058-7.7011.6895.1137.9672.892-.135-.353x6-.38010.541.4893.7892.8925.493
11、-.0822.196x7-.011.719-.055-.219-.135-.082.287.067x8.27713.319.0221.964-.3532.196.0673.614a. 总的协方差矩阵的自由度为 26。该表给出各类的协方差矩阵和总协方差矩阵。表2：有关典型判别函数的输出表特征值函数特征值方差的 %累积 %正则相关性14.937a100.0100.0.912a. 分析中使用了前 1 个典型判别式函数。Wilks 的 Lambda函数检验Wilks 的 Lambda卡方dfSig.1.16837.4048.000 标准化的典型判别式函数系数函数1x1.376x2.891x3-.118
12、x41.006x5-.644x6.393x7.372x8-.310结构矩阵函数1x2.733x8.349x6.274x4.238x1.120x7.078x5-.056x3.021判别变量和标准化典型判别式函数之间的汇聚组间相关性 按函数内相关性的绝对大小排序的变量。组质心处的函数类别函数11-1.26523.614在组均值处评估的非标准化典型判别式函数分类处理摘要已处理的27已排除的缺失或越界组代码0至少一个缺失判别变量0用于输出中27组的先验概率类别先验用于分析的案例未加权的已加权的1.5002020.0002.50077.000合计1.0002727.000解：由上可得：分类函数系数类别12x16.8308.051x22.1322.997x3-
