第十讲 MINITAB在回归设计试验结果统计分析中的应用
董占山
(中国农科院棉花所,河南安阳,455112)
回归设计在农业科学试验中的应用已经十分广泛,但是其试验结果的统计分析一直困扰着农业科技工作者。以往,大家或者自己用计算器一步一步计算,或者用BASIC编写一些程序,用来计算回归方程,但是,这解决不了根本的问题。那么能否用MINITAB来分析回归设计试验结果呢?回答是肯定的。本文讲解用MINITAB对常用回归设计试验结果进行统计分析的方法。为了节约篇幅,本文以实例为基础讲解用MINITAB对二次正交旋转回归设计、二次通用旋转回归设计、最优回归设计的试验结果的统计分析方法和对回归方程的频率分析方法,具体的计算方法读者可以查阅有关书籍。
二次正交旋转回归设计试验结果的统计分析
〖例1〗为了研究水稻新品种高产栽培技术措施,采用五元二次正交旋转回归设计,对一水稻新品种进行多因素栽培技术试验,试验的结构矩阵和结果见表10-1所示,试用MINITAB进行统计分析。
表10-1
水稻新品种回归设计试验的结构矩阵和试验结果
试验号 |
X1 |
X2 |
X3 |
X4 |
X5 |
产量 |
1 |
-1 |
-1 |
-1 |
-1 |
1 |
976 |
2 |
1 |
-1 |
-1 |
-1 |
-1 |
872 |
3 |
-1 |
1 |
-1 |
-1 |
-1 |
826 |
4 |
1 |
1 |
-1 |
-1 |
1 |
919 |
5 |
-1 |
-1 |
1 |
1 |
1 |
868 |
6 |
1 |
-1 |
1 |
1 |
-1 |
705 |
7 |
-1 |
1 |
1 |
1 |
-1 |
820 |
8 |
1 |
1 |
1 |
1 |
1 |
751 |
9 |
-1 |
-1 |
1 |
-1 |
-1 |
855 |
10 |
1 |
-1 |
1 |
-1 |
1 |
721 |
11 |
-1 |
1 |
1 |
-1 |
1 |
931 |
12 |
1 |
1 |
1 |
-1 |
-1 |
710 |
13 |
-1 |
-1 |
-1 |
1 |
-1 |
948 |
14 |
1 |
-1 |
-1 |
1 |
1 |
934 |
15 |
-1 |
1 |
-1 |
1 |
1 |
951 |
16 |
1 |
1 |
-1 |
1 |
-1 |
839 |
17 |
-2 |
0 |
0 |
0 |
0 |
899 |
18 |
2 |
0 |
0 |
0 |
0 |
855 |
19 |
0 |
-2 |
0 |
0 |
0 |
879 |
20 |
0 |
2 |
0 |
0 |
0 |
832 |
21 |
0 |
0 |
-2 |
0 |
0 |
867 |
22 |
0 |
0 |
2 |
0 |
0 |
744 |
23 |
0 |
0 |
0 |
-2 |
0 |
798 |
24 |
0 |
0 |
0 |
2 |
0 |
879 |
25 |
0 |
0 |
0 |
0 |
-2 |
845 |
26 |
0 |
0 |
0 |
0 |
2 |
879 |
27 |
0 |
0 |
0 |
0 |
0 |
850 |
28 |
0 |
0 |
0 |
0 |
0 |
873 |
29 |
0 |
0 |
0 |
0 |
0 |
823 |
30 |
0 |
0 |
0 |
0 |
0 |
857 |
31 |
0 |
0 |
0 |
0 |
0 |
867 |
32 |
0 |
0 |
0 |
0 |
0 |
880 |
33 |
0 |
0 |
0 |
0 |
0 |
850 |
34 |
0 |
0 |
0 |
0 |
0 |
865 |
35 |
0 |
0 |
0 |
0 |
0 |
862 |
36 |
0 |
0 |
0 |
0 |
0 |
840 |
MINITAB程序
# EXAMPLE 10-1
SET C1 #X0
(1)36
END
SET C2 # X1
8(-1 1) (-2 2) (0)18
END
SET C3 # X2
4(-1 1)2 (0)2 (-2 2) (0)16
END
SET C4 # X3
(-1 1)4 (1 -1)4 (0)4 (-2 2) (0)14
END
SET C5 # X4
2(-1 1)4 (0)6 (-2 2) (0)12
END
SET C6 # X5
2(1 -1 -1 1) 2(-1 1 1 -1) (0)8 (-2 2) (0)10
END
LET C7=C2*C3 # X1X2
LET C8=C2*C4 # X1X3
LET C9=C2*C5 # X1X4
LET C10=C2*C6 # X1X5
LET C11=C3*C4 # X2X3
LET C12=C3*C5 # X2X4
LET C13=C3*C6 # X2X5
LET C14=C4*C5 # X3X4
LET C15=C4*C6 # X3X5
LET C16=C5*C6 # X4X5
LET C17=C2*C2 # X1**2
LET C18=C3*C3 # X2**2
LET C19=C4*C4 # X3**2
LET C20=C5*C5 # X4**2
LET C21=C6*C6 # X5**2
LET C17=C17 - SUM(C17)/N(C17)
LET C18=C18 - SUM(C18)/N(C18)
LET C19=C19 - SUM(C19)/N(C19)
LET C20=C20 - SUM(C20)/N(C20)
LET C21=C21 - SUM(C21)/N(C21)
SET C22
976 872 826 919 868 705 820 751 855 721 931 710 948
934 951 839 899 855
879 832 867 744 798 879 845 879 850 873 823 857 867
880 850 865 862 840
END
COPY C1-C21 M1
TRAN M1 M2
MULT M2 C22 M3
LET K1=SUM(C1*C1)
LET K2=SUM(C2*C2)
LET K3=SUM(C3*C3)
LET K4=SUM(C4*C4)
LET K5=SUM(C5*C5)
LET K6=SUM(C6*C6)
LET K7=SUM(C7*C7)
LET K8=SUM(C8*C8)
LET K9=SUM(C9*C9)
LET K10=SUM(C10*C10)
LET K11=SUM(C11*C11)
LET K12=SUM(C12*C12)
LET K13=SUM(C13*C13)
LET K14=SUM(C14*C14)
LET K15=SUM(C15*C15)
LET K16=SUM(C16*C16)
LET K17=SUM(C17*C17)
LET K18=SUM(C18*C18)
LET K19=SUM(C19*C19)
LET K20=SUM(C20*C20)
LET K21=SUM(C21*C21)
COPY K1-K21 C23
COPY M3 C24
LET C25=C24/C23 # BIJ,回归系数
PRINT C25
LET C26=C24**2/C23 # UIJ
DELE 1 C26
LET K1=SUM(C26) # U
LET K2=STDEV(C22)**2 * (N(C22)-1) # SSY
COPY C22 C27
DELE 1:26 C27
LET K5=STDEV(C27)**2 * (N(C27)-1)
LET K6=(K2-K1)-K5
LET K3=(K1/N(C26)) / (K5/9) # FU,回归F值
PRINT K3
LET C28=C26/(K5/9)
PRINT C28
LET K4=K1/K2 # R**2
PRINT K4
LET K7=K6/6/(K5/9) # FE,失拟F值
PRINT K7
# END
MINITAB计算结果
在程序中已经注明了回归项的顺序,在此只列出计算结果,不再重复给出各回归项的名称。
C25(方程的回归系数)
851.944 -33.833 -9.417 -47.917 7.000 22.667 6.625
-28.125 0.500 -4.875 16.125 -3.500 14.875 -9.500
-7.125 -5.750 6.104 0.729 -11.771 -3.521 2.354
K3 23.0452 (**,回归方程的F值)
C28(回归系数的F值)
98.269 7.612 197.106 4.207 44.106 2.512 45.271
0.014 1.360 14.881 0.701 12.663 5.165 2.905
1.892 4.265 0.061 15.859 1.419 0.634
K4 0.883089(决定系数)
K7 8.66969(**,失拟F值)
二次通用旋转回归设计试验结果的统计分析
〖例2〗为了研究大豆的高产栽培技术措施,选用四个主要栽培措施为试验因子,采用四元二次通用旋转组合设计,试验的结构矩阵和结果见表10-2所示。试用MINITAB对试验结果进行统计分析。
表10-2 大豆高产栽培技术试验结果
试验号 |
X1 |
X2 |
X3 |
X4 |
产量 |
1 |
1 |
1 |
1 |
1 |
348 |
2 |
1 |
1 |
1 |
-1 |
352 |
3 |
1 |
1 |
-1 |
1 |
357 |
4 |
1 |
1 |
-1 |
-1 |
328 |
5 |
1 |
-1 |
1 |
1 |
348 |
6 |
1 |
-1 |
1 |
-1 |
377 |
7 |
1 |
-1 |
-1 |
1 |
343 |
8 |
1 |
-1 |
-1 |
-1 |
395 |
9 |
-1 |
1 |
1 |
1 |
377 |
10 |
-1 |
1 |
1 |
-1 |
382 |
11 |
-1 |
1 |
-1 |
1 |
351 |
12 |
-1 |
1 |
-1 |
-1 |
381 |
13 |
-1 |
-1 |
1 |
1 |
372 |
14 |
-1 |
-1 |
1 |
-1 |
341 |
15 |
-1 |
-1 |
-1 |
1 |
328 |
16 |
-1 |
-1 |
-1 |
-1 |
371 |
17 |
2 |
0 |
0 |
0 |
329 |
18 |
-2 |
0 |
0 |
0 |
341 |
19 |
0 |
2 |
0 |
0 |
358 |
20 |
0 |
-2 |
0 |
0 |
357 |
21 |
0 |
0 |
2 |
0 |
332 |
22 |
0 |
0 |
-2 |
0 |
376 |
23 |
0 |
0 |
0 |
2 |
374 |
24 |
0 |
0 |
0 |
-2 |
316 |
25 |
0 |
0 |
0 |
0 |
435 |
26 |
0 |
0 |
0 |
0 |
387 |
27 |
0 |
0 |
0 |
0 |
396 |
28 |
0 |
0 |
0 |
0 |
400 |
29 |
0 |
0 |
0 |
0 |
382 |
30 |
0 |
0 |
0 |
0 |
390 |
31 |
0 |
0 |
0 |
0 |
386 |
MINITAB程序
# EXAMPLE 10-2
SET C1 # X0
(1)31
END
SET C2 # X1
(1 -1)8 (2 -2) (0)13
END
SET C3 # X2
2(1 -1)4 (0)2 (2 -2) (0)11
END
SET C4 # X3
4(1 -1)2 (0)4 (2 -2) (0)9
END
SET C5 # X4
8(1 -1) (0)6 (2 -2) (0)7
END
LET C6 = C2*C3 # X1X2
LET C7 = C2*C4 # X1X3
LET C8 = C2*C5 # X1X4
LET C9 = C3*C4 # X2X3
LET C10 = C3*C5 # X2X4
LET C11 = C4*C5 # X3X4
LET C12 = C2*C2 # X1X1
LET C13 = C3*C3 # X2X2
LET C14 = C4*C4 # X3X3
LET C15 = C5*C5 # X4X4
SET C16 # Y
348 352 357 328 348 377 343 395 377 382 351 381 372
341 328
371 329 341 358 357 332 376 274 316 435 387 396 400
382 390 386
END
COPY C1-C15 M1
TRAN M1 M2
MULT M2C16 M3
COPY M3 C17 # SUM(XY)
LET K1 = 0.1428571 # K
LET K2 = -0.0357142 # E
LET K3 = 0.0349702 # F
LET K4 = 0.00372023 # G
LET K10 = K1*C17(1) + K2 * (C17(12) + C17(13)
+C17(14) + C17(15)) # B0
LET K11 = C17(2) / 24 # B1
LET K12 = C17(3) / 24 # B2
LET K13 = C17(4) / 24 # B3
LET K14 = C17(5) / 24 # B4
LET K15 = C17(6) / 16 # B12
LET K16 = C17(7) / 16 # B13
LET K17 = C17(8) / 16 # B14
LET K18 = C17(9) / 16 # B23
LET K19 = C17(10) / 16 # B24
LET K20 = C17(11) / 16 # B34
LET K30 = K4 * (C17(12) + C17(13) +C17(14) +
C17(15)) + K2 * C17(1)
LET K21 = (K3 -K4) * C17(12) + K30 # B11
LET K22 = (K3 -K4) * C17(13) + K30 # B22
LET K23 = (K3 -K4) * C17(14) + K30 # B33
LET K24 = (K3 -K4) * C17(15) + K30 # B44
COPY K10-K24 C18 # BIJ
NAME C18 'BIJ'
PRINT C18
LET K51 = STDEV(C16)**2 * (N(C16)-1) # SSY
LET K61 = N(C16) - 1 # DFT
LET K52 = SUM(C17*C18)
LET K53 = SUM(C16*C16) - K52 # Q
LET K52 = K51- K53 # U
LET K62 = N(C17) - 1 # DFU
LET K63 = K61 - K62 # DFQ
COPY C16 C20
DELE 1:24 C20
LET K54 = STDEV(C20)**2 * (N(C20)-1) # SSE
LET K64 = N(C20) - 1 # DFE
LET K55 = K53 - K54 # SSLF
LET K65 = K63 - K64 # DFLF
LET K70 = (K55/K65) / (K54/K64) # FLF,失拟F值
LET K71 = (K52/K62) / (K53/K63) # FU1,以离回归Q为分母的F值
LET K72 = (K52/K62) / (K54/K64) # FU2,以误差为分母的F值
PRINT K70-K72
# END
MINITAB计算结果
bij(回归系数)
396.574 -3.292 0.125 -1.875 -7.792 -9.813 -2.437
-0.563 2.562 5.187 5.563 -11.382 -5.757 -6.632
-21.382
K70 2.04048 (失拟F值)
K71 2.73302 (以离回归Q为分母的F值)
K72 4.51031 (以误差为分母的F值)
最优回归设计试验结果的统计分析
〖例3〗有一小麦施用氮、磷肥盆栽试验,采用D-饱和最优回归设计,无重复。试验结构矩阵及试验结果见表10-3所示。试用MINITAB对试验结果进行统计分析。
表10-3 小麦二因素D-饱和最优回归设计试验结果
试验号 |
X1 |
X2 |
产量 |
1 |
-1 |
-1 |
15.5 |
2 |
1 |
-1 |
17.54 |
3 |
-1 |
1 |
17.18 |
4 |
-0.1315 |
-0.1315 |
18.3 |
5 |
1 |
0.3945 |
17.68 |
6 |
0.3945 |
1 |
18.7 |
MINITAB程序
# EXAMPLE 10-3, 最优设计
SET C1 # X0
(1)6
END
SET C2 # X1
-1 1 -1 -0.1315 1 0.3945
END
SET C3 # X2
-1 -1 1 -0.1315 0.3945 1
END
LET C4=C2*C2 # X1X1
LET C5=C3*C3 # X2X2
LET C6=C2*C3 # X1X2
SET C7 # Y
15.5 17.54 17.18 18.30 17.68 18.7
END
COPY C1-C6 M1
TRAN M1 M2
MULT M2 M1 M3
INVE M3 M5 # M5=C MATRIX,相关矩阵
MULT M2 C7 M4
COPY M4 C8 # B VALUES
MULT M5 C8 C9
PRINT C9 # BIJ, 回归系数
# END
MINITAB计算结果
C9(回归系数)
18.4734 0.6241 0.4441 -1.4232 -0.0861 -0.3959
对回归方程进行频率分析的方法
〖例4〗有一玉米高产栽培试验,研究水分、肥料、密度等栽培因素对叶面积指数的综合影响,采用4因素5水平二次通用旋转回归设计,所得的回归方程如下:
Y=3.561 + 0.124X1 - 0.112X2 - 0.015X3 + 0.812X4 -
0.09X1X2 + 0.09X1X3 + 0.19X1X4 + 0.045X2X3 - 0.105X2X4 - 0.075X3X4 - 0.039X1X1 + 0.027X2X2
+ 0.05X3X3 - 0.147X4X4
试以该回归方程为基础进行频率分析,求出叶面积系数大于5的技术措施方案。
MINITAB程序
# EXAMPLE 10-4
SET C1 # X1
125(-2 -1 0 1 2)
END
SET C2 # X2
25(-2 -1 0 1 2)5
END
SET C3 # X3
5(-2 -1 0 1 2)25
END
SET C4 # X4
(-2 -1 0 1 2)125
END
LET K50 = 3.561
LET K1= 0.124
LET K2= -0.112
LET K3= -0.015
LET K4= 0.812
LET K11= -0.039
LET K12= -0.09
LET K13= 0.09
LET K14= 0.19
LET K22= 0.027
LET K23= 0.045
LET K24= -0.105
LET K33= 0.05
LET K34= -0.075
LET K44= -0.147
LET C5 = K50 + K1*C1 + K2*C2 + K3*C3 + K4*C4 +
K11*C1**2 + K12*C1*C2 + K13*C1*C3 + K14*C1*C4 + K22*C2**2 + K23*C2*C3 + K24*C2*C4 +
K33*C3**2 + K34*C3*C4 + K44*C4**2
SORT C1-C5 C6-C10;
BY C5.
LET C11=C10>5
UNSTACK (C6-C10) (C16-C20) (C26-C30);
SUBSCRIPTS C11.
DESC C26-C30
#END
MINITAB计算结果
N MEAN MEDIAN TRMEAN STDEV SEMEAN
C26 62 1.226 1.000 1.304 0.876 0.111
C27 62 -1.161 -1.000 -1.232 0.909 0.115
C28 62 -0.177 0.000 -0.196 1.499 0.190
C29 62 1.7097 2.0000 1.7321 0.4576 0.0581
C30 62 5.5920 5.4925 5.5608 0.4800 0.0610
上述计算结果中,MEAN项下的值就是各个因子的平均最优编码值水平(如果有编码与因子实际取值的转换表,可以将这些编码转换为实际值),采用这样的因素水平组合,预期可以获得的叶面积指数为5.592。
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