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* 数据分析报告 *

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回归数据

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第一维      第二维

5                  3.3        

80               29.2       

15               9.2        

25               14.5       

90               21         

30               17         

95               12         

35               19.4       

40               21.7       

50               25.9       

60               29.2       

20               11.9       

55               27.6       

65               30         

70               30.3       

45               23.9       

75               30         

10               6.5        

85               26         

 

回归计算基本条件

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                 回归计算数据的维数是:2

                 回归数据的样本容量是:19

                          回归计算的元数是:8

                                幂的初值设定是:1

                 回归计算的精度设置是:0.01

模型中变量和数据中维对应关系:第一维(x1),第二维(y)

                        回归此数据的模型是:二维非线性多项式(y = a0 + a1 * x1 ^ 1 + a2 * x1 ^ 2 + ......+ an * x1 ^ n)

 

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:0.994171793407449

            统计量 F 值是:-1615.76787237647

 剩余标准偏差  S 值是:0.914156871112958

                   最大误差是:14.3876760235299

                   平均误差是:1.93832422733425

    平均相对误差(%)是:0.101696956253789

 

回归模型系数

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 a0 = -1.73898264421671

 a1 = 0.842370691283978

 a2 = -0.00575053470170359

 a3 = 2.12096286332876E-11

 a4 = -3.10374977485252E-15

 a5 = -1.34822201505489E-28

 a6 = 7.32403555472667E-53

 a7 = -1.24033444706402E-62

 a8 = -2.54371664160135E-110

 

回归模型幂值

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模型中无幂值

 

第一维(x1)   目标函数(y)    模型计算目标函数值    模型计算绝对误差        模型计算相对误差(%)  

5                      3.3                     2.32910740500747          0.970892594992532       0.416851791765876    

80                   29.2                    28.8472617898247          0.352738210175339       0.0122277882991224   

15                   9.2                       9.60270744486936          -0.402707444869359    0.0419368649082943   

25                   14.5                    15.7262007566511          -1.22620075665107       0.0779718366581625   

90                   21                        27.4950644013917          -6.4950644013917          0.236226557122119    

30                   17                        18.3566574288434          -1.35665742884344       0.0739054718486905   

95                   12                        26.3876760235299          -14.3876760235299       0.545242256676958    

35                   19.4                    20.6995874661206          -1.29958746612057       0.0627832544125642   

40                   21.7                    22.7549908840871          -1.05499088408705       0.0463630545694847   

50                   25.9                   26.0032179242737          -0.103217924273711      0.00396942888277524  

60                   29.2                    28.1013386733091          1.09866132669087        0.0390964053158929   

20                   11.9                    12.8082174338923          -0.908217433892279      0.0709089643878947   

55                   27.6                    27.1960415774703          0.403958422529687       0.0148535742372277   

65                   30                        28.719109227162           1.28089077283801          0.0446006442158927   

70                   30.3                    29.0493532543541          1.25064674564586          0.043052481571458    

45                   23.9                    24.5228676983009          -0.622867698300933      0.0253994641231983   

75                   30                        29.0920707701643          0.907929229835684       0.031208821022353    

10                   6.5                       6.10967077383803          0.39032922616197          0.0638871128430328   

85                   26                        28.3149263285208          -2.31492632852078         0.081756395960989    

 

 

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           分析日期:2021-12-23 14:01:39