< 实例-多元数据回归报告

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* 数据分析报告 *

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回归数据

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7            26          6            60         78.5       

1            29          15          52         74.3       

11          56          8            20         104.3      

11          31          8            47         87.8       

7            52          6            33         95.9       

11          55          9            22         109.2      

3            71          17          6           102.7      

1            31          22          14         72.5       

2            54          18          22         93.1       

21          47          4            26         115.9      

1            40          23          34         83.8       

11          66          9            12         113.3      

10          68          8            12         109.4

 

回归计算基本条件

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                   回归计算数据的维数是:5

                   回归数据的样本容量是:13

                            回归计算的元数是:4

                                 幂的初值设定是:不设初值

                   回归计算的精度设置是:无

模型中变量和数据中维对应关系:

                        回归此数据的模型是:多维多元线性(y = a0 + a1 * x1 + a2 * x2 + ...... an * xn)

 

回归数据预处理情况

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数据没有做预处理

 

回归模型检验参数

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           相关系数 R 值是:0.994437836653453

                统计量 F 值是:-1158.86911614657

 剩余标准偏差  S 值是:1.521454571197

                    最大误差是:3.96796656847

                    平均误差是:1.12607325223776

     平均相对误差(%)是:0.0113627437503438

 

回归模型系数

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 a0 = 29.7788565438446

 a1 = 1.95947606540792

 a2 = 0.80986780255038

 a3 = 0.598332565814905

 a4 = 0.180509887831205

 

回归模型幂值

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模型中无幂值

回归模型误差详情

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目标函数        目标函数计算值              绝对误差                              相对误差(%)

78.5                  78.9723410606384          -0.472341060638428          0.00598109482756429 

74.3                  73.5860018730164           0.71399812698364             0.00970290692264746 

104.3               105.082548856735           -0.782548856735232          0.00744699158184794 

87.8                  89.7096210420132           -1.90962104201317            0.0212866916595139  

95.9                  95.1551366746426           0.744863325357443           0.00782788351094805 

109.2               105.23203343153              3.96796656847                    0.0377068316469603  

102.7               104.412611663342           -1.71261166334152            0.0164023448514391  

72.5                 72.5346895456314           -0.0346895456314087        0.00047824766120472 

93.1                 92.1718738675117           0.928126132488245           0.0100695157160669  

115.9               116.078228831291           -0.178228831291193          0.00153541997569787 

83.8                 84.0320302248001            -0.232030224800113         0.00276121169724678 

113.3               112.33548027277             0.964519727230069           0.00858606492702082 

109.4              111.39740717411             -1.99740717411041             0.0179304637763115  

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           分析日期:2021-12-23 13:53:59