大鲵体重与体型关系模型(3)

附录一：

clc clear

%大鲵体重与体型关系研究

%研究找出直接影响大鲵体重的主要指标:

weight=[9.6 13.8 15.7 13 22.6 10.7 10.4 8.6 9.5 10.4 10 12 15.1 13.5 16 9.9 11.7 11.1 10.6 14.2 11.2 11.9 12.4 12.9 11.1 13.9 11 22.8 47.3 9.6 24.9 19.7 18.1 49.6 26.8 13.9 13.2 15.9 10.4 13.8 19.3 12.8 11 12.2 15.6 12.8 14.6 9.9 9.4 26.8 16.6 17.7 9.5 11.9 11.3 13.6 9 101.2 15.3 64.9 17.4 17.1 15.6 12.2 14.3 64.2 15.6 16.9 13.1 13.6 12.3 11.7 20.3 11.6 3.4 14.1 10.7 14.9 15.3 12.3 13.1 14.5 11.9 21.1 16.5 13.1 16.5 12 11.6 12 9.7 12.3 8.8 15.4 11.6 33.1 17.7 6.1 5.9 7.2 55.8 4.6 5.8 11.5 6.9 5.8 5.9 8.5 8.7 28.9 6.1 11.5 8.4 8.3 3.4 8.8 9.1 8.6 8.9 10.4 7 11.7 8.9 10.3 8.7 9.2 10.1 4.6 6 6.8 7.3 6.1 7.5 4.9 4.6 6.1 4.6 5.9 8.1 4.4 3.7 5.5 4 4.7 6.4 3.7 4.1 2.8 4.5 5.7 3.4 3.7 3.5 3.6 3 3.8 2.7 4.1 3.2 3.4 2.5 3.5 3.4 3.4 2.3 2.4 2.4 2.5 3 2.7 2.4 3 ];

length1=[8.8 10.2 10.5 9.5 10.9 9.3 9.1 8.5 9 9.2 8.9 10 10 9.7 9.8 9.5 9.5 9.4 9.3 10.3 9.6 9.7 9.8 9.5 9.4 9.4 9.5 11.6 15.5 8.6 12.2 11.2 11.2 14.9 12.4 9.9 10.4 10.1 9 10.1 11.3 10 9.1 9.3 10.5 9.5 10.4 9.3 8.6 12.2 11 10.7 8.8 9.7 9.4 9.8 9 18.4 10.8 15.1 11 10.9 10.6 10 10.3 15.9 10.7 11.6 10.1 10.1 9.8 9.7 11.6 9.9 13.7 10.3 10 10.8 10.7 10.1 10.3 10.4 9.7 11.4 10.9 10.2 10.8 9.9 9.7 10.1 9.4 10.3 9.2 10.6 9.8 13.9 10.6 7.5 7.6 7.9 15.8 7 7.4 9.9 7.8 8.2 7.8 8.4 8.9 13 7.6 9.1 8.7 8.6 6.5 9.6 8.3 8.5 8.5 9.6 8.1 9.6 8.5 9.7 8.6 8.9 9 7.1 9.6 7.5 8.5 8.1 8.5 7.1 7.1 7.6 6.9 8 8.5 6.9 6.8 7.6 6.7 7 7.7 6.6 6.6 6.2 7.1 7.4 6.3 6.6 6.2 6.5 6.3 6.6 6.2 6.9 6.6 6.4 5.9 6.5 6.3 6.5 5.7 5.7 5.7 5.9 6.2 6.05 5.8 6 ];

length2=[7.6 8.2 9.2 7.6 9.2 7.6 7.5 6.9 7.1 7.5 7.4 8.5 8.5 8.1 8.7 7.7 7.6 8 8.1 8.7 8 8.1 8.1 8.2 7.6 8.1 7.9 9.5 12.6 6.9 10.3 8.8 9.3 12.7 10.3 8.1 8.3 8.5 7.4 8.3 9.4 8.1 7.6 7.8 8.5 7.7 8.6 7.6 7.2 10 8.9 8.7 7.3 7.9 7.6 8.2 7.4 15.2 8.9 12.5 9.3 8.9 8.8 8.3 8.7 12.3 8.8 9.6 8.3 8.3 8.2 7.9 9.7 8.2 11.1 8.6 8.3 8.8 8.7 8.3 8.7 8.6 7.8 9.5 8.9 8.4 8.9 8.2 8.2 8.3 7.6 8.7 7.7 8.7 8.2 11.6 8.8 6.1 6.1 6.4 13.1 5.6 5.9 7.9 6.3 6.8 6.3 6.9 7.3 10.6 6.4 7.6 7.2 7 5.4 7.9 6.8 6.8 6.9 7.8 6.8 7.8 7.4 8.2 7.1 7.2 7.3 5.6 6.4 5.9 7 6.7 6.9 5.8 5.8 6.2 5.6 6.3 7.1 5.6 5.4 6.4 5.6 5.8 6.2 5.3 5.4 5.1 5.3 5.9 5.2 6.2 5.1 5.2 5.1 5.3 4.9 5.6 5.1 5.3 4.6 5.4 5.2 5.3 4.9 4.6 4.4 4.7 5.1 4.8 4.6 4.8

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];

length3=[2.3 2.9 3.1 2.7 3.5 2.5 2.6 2.4 2.6 2.8 2.6 3.2 3 2.9 2.9 2.6 2.9 2.9 2.7 3 2.7 2.9 3.1 2.9 2.8 3.1 2.8 3.3 4.1 2.4 3 3.2 3.3 4.3 3.8 2.9 2.8 2.6 2.5 2.8 3.3 2.6 2.8 2.7 2.9 2.9 3.1 2.6 2.4 3.6 3.1 2.9 2.5 3.1 2.7 2.9 2 5.4 3 4.4 3.2 3.3 3.2 2.6 2.7 4.3 3.1 3.1 2.9 3 2.8 2.9 3.2 2.8 3.4 2.8 3 3.1 3 3 2.8 2.5 2.6 3.3 3.2 3.1 3.1 2.7 2.8 2.8 2.7 2.9 2.8 3.1 2.9 4 3.02 2.02 2.04 2.15 4.5 2 2.1 2.75 2.05 2.3 2.2 2.2 1.7 3.4 2.1 2.4 2.35 2.43 1.8 2.45 2.4 2.2 2.4 2.7 2.2 2.6 2.4 2.7 2.5 2.5 2.4 2.1 2.1 2.1 2.3 2.3 2.4 2.1 2.1 2.2 2.1 2.1 2.1 2.1 1.8 2.2 1.8 1.9 2.1 1.8 1.8 1.75 2 2.1 1.7 2 1.9 1.8 1.7 1.9 1.7 1.8 1.7 1.75 1.9 1.9 1.9 1.9 1.6 1.6 1.6 1.8 1.8 1.7 1.7 1.7 ];

length4=[1.6 2.1 1.8 1.8 2.1 1.7 1.6 1.3 1.6 1.7 1.6 1.6 1.7 1.6 1.8 1.5 1.6 1.6 1.6 1.7 1.6 1.8 1.8 1.7 1.5 1.6 1.5 2.3 2.6 1.5 2.3 1.9 1.7 2.5 2.3 2 2.7 1.8 1.5 1.6 1.7 1.5 1.4 1.4 1.6 1.7 1.6 1.3 1.5 2 1.8 1.7 1.5 1.6 1.6 1.7 1.4 3.3 1.5 2.3 1.5 1.6 1.5 1.2 1.4 2.6 1.4 1.4 1.3 1.2 1.2 1.1 1.4 1.2 1.9 1.2 1 1.3 1.4 1.2 1.4 1.4 1.9 1.5 1.3 1.2 1.4 1.2 1.2 1.1 1 1.3 1.1 1.4 1.3 2.03 2.06 1.5 1.34 1.7 3.01 1.3 1.2 1.8 1.6 1.25 1.3 1.4 2.3 2.5 1.4 1.8 1.5 1.7 1 1.4 1.7 1.6 1.7 1.6 1.3 1.7 1.6 1.6 1.5 1.5 1.6 1.2 1.3 1.4 1.2 1.3 1.3 1.2 1.2 1.4 1.3 1.25 1.5 1.1 1.1 1.3 1.1 1.2 1.3 1.2 1.2 1 1.1 1.4 1 1.2 1 1.1 1 1.1 1.1 1.1 1 1.7 1 1.1 1.2 0.9 0.9 0.9 0.9 1.1 1.2 1 1 1.1 ];

length5=[1.8 1.9 2.2 1.9 2.5 1.6 2 1.4 1.8 1.8 2 1.9 2.2 2.2 2.3 1.9 1.9 1.8 1.9 2.2 2 1.9 2 2 1.7 2.1 1.8 2.4 3.2 1.8 2.6 2.3 2.2 3.4 2.4 1.9 2 2.1 1.7 2 2.2 2 1.8 1.8 2.2 1.9 1.9 1.7 1.8 2.5 2.1 2.1 1.7 1.9 2 1.8 1.6 4.3 2.2 3.6 2.2 2.1 2.2 2 2.1 3.5 2.2 2.2 1.9 2.1 1.9 2 2.4 1.8 2.8 2 2 2.1 2.1 2 1.9 2.1 2 2.5 2.2 1.8 2.2 1.8 1.8 1.9 1.8 2 1.8 2.1 1.9 2.57 2.05 1.45 1.3 1.7 3.3 1.3 1.4 1.7 1.6 1.3 1.54 1.7 1.5 2.5 1.4 1.8 1.5 1.6 1.15 1.6 1.7 1.7 1.7 1.8 1.5 1.8 1.5 1.7 1.6 1.8 1.6 1.3 1.4 1.4 1.6 1.4 1.6 1.4 1.3 1.4 1.3 1.2 1.6 1.3 1.1 1.4 1.3 1.4 1.6 1.2 1.3 1.1 1.2 1.3 1.1 1.2 1.2 1.2 1.1 1.1 1.1 1.4 1.1 1.2 1 1.2 1.1 1.2 1 1.1 1.1 1 1.1 1.1 1.1 1.2 ];

length6=[1.5 1.8 1.9 1.8 1.7 1.7 1.4 1.7 1.6 1.3 1.3 1.7 1.6 1.8 2.1 1.6 1.6 1.6 1.5 1.4 1.6 1.5 1.6 1.5 1.5 1.6 1.5 1.8 2.6 1.5 1.8 1.8 1.7 2.5 2.5 1.6 1.5 1.8 1.5 1.7 1.6 1.6 1.5 1.5 1.8 1.6 1.7 1.6 1.4 2.2 1.7 1.8 1.4 1.6 1.6 1.4 1.4 2.6 1.7 2.3 1.6 1.6 1.5 1.7 1.6 2.5 1.5 2 1.4 1.7 1.6 1.5 2.1 1.7 2.2 1.6 1.6 1.6 1.5 1.5 1.6 1.7 1.5 1.8 1.6 1.6 1.7 1.6 1.7 1.5 1.4 1.7 1.5 1.6 1.5 2.48 1.6 1.26 1.1 1.54 2.2 1.3 1.15 1.8 1.45 1.35 1.2 1.4 1.4 2.2 1.4 1.6 1.5 1.4 1 1.8 1.4 2 1.6 1.7 1.6 1.6 1.8 1.5 1.6 1.6 1.35 1.3 1.4 1.2 1.4 1.45 1.7 1.4 1.5 1.4 1.4 1.5 1.6 1.3 1.2 1.5 1.1 1.4 1.4 1.3 1.2 1.3 1.5 1.3 1.1 1.3 1.3 1.4 1.3 1.2 1.2 1.3 1.1 0.9 1.2 1.4 1.3 1.4 1.2 1.15 1.1 1.2 1.3 1 1.1 1.2 ];

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length7=[7.8 7.8 10 9.3 12.8 9.8 10.1 9.2 9.3 9.6 10.2 10.1 10.4 6.2 10 10.3 10.2 7.8 9.7 10.8 10.5 9.7 10.8 9.7 8.3 8.5 10.3 10.5 16 9.6 10.5 12.1 12 11.8 12 9.2 9.2 7.1 10.3 8.9 6.5 6.2 11.1 10.5 7.1 6.7 7.7 7.9 9.1 7.3 12 11.1 7.2 6.4 8.2 8.9 6.1 14.2 10.5 13.9 11.2 8 8.6 9.6 9.6 15.5 5.8 6.1 11.8 6.1 8.8 5.5 8.6 8 12 8 8 9.4 8.5 4.5 6.6 9.3 6.9 9.5 8.4 5.3 9.4 8.1 8.3 7.5 8.3 8 4.8 10.2 6 10.4 10.3 6.2 5.6 5.3 9.7 3.4 4.4 5.2 4.6 7.4 4.2 6.1 4.3 6.7 4.3 7.2 4.7 3.9 5.7 6.8 5.4 4.7 4.4 4.8 4.4 4.8 7.2 6.7 7.4 6.7 6.7 4 7.1 7.6 6 4 6.7 5 5.1 7.9 6 6.8 7.8 5 3.6 4.8 6.7 4.8 5.6 4.5 4.9 4.2 5.3 7.2 5.3 6.1 6.5 3.9 4.5 4.3 4.4 3.7 5 4.6 5.2 5 4.6 5.6 4.7 4.8 3.9 4.2 6.1 4.8 4.2 4.4 ];

alpha=0.05;

X=[length1' length2' length3' length4' length5' length6' length7']; [b,bint,r, rint,starts]=regress(Y,X,alpha); subplot(2,2,1),

rcoplot(r,rint);%残差分析 %找出异常数据 I=[];

for i=1:length(r) if 0>rint(i,2) I=[I i]; elseif 0

X(j,:)=X(173-i,:); Y(j,:)=Y(173-i,:); end end end length(Y)

NX=X(1:167,:); NY=Y(1:167,:);

[b1,bint1,r1, rint1,starts1]=regress(NY,NX,alpha); subplot(2,2,2),

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rcoplot(r1,rint1);%残差分析 %找出异常数据 I=[];

for i=1:length(r1) if 0>rint1(i,2) I=[I i]; elseif 0

附录二：

%对其他指标的最优回归方程求解 Y=[ weight'];

X=[length4' length6' length7'];

[b,se,pval,inmodel,stats,nextstep,history]=stepwisefit(X,Y,'penter',0.05,'display','off');

%自变量的筛选和模型参数估计信息 inmodel,b0=stats.intercept,b %回归方程显著性整体检验信息

ALLp=stats.pval,rmse=stats.rmse %回归方程显著性分别检验信息 P=stats.PVAL %rmse为标准误差估计

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