数学建模：相关性分析

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数学建模：相关性分析

相关性分析

两变量的相关分析

Pearson

Spearman

Kendall tua-b

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双变量关系强度测量的指标

相关系数的性质

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代码实现example

%% Pearson
clc;clear;

data = [2.00 46.00
        3.00 42.00
        4.00 41.00
        3.00 43.00
        4.00 39.00
        5.00 38.00
        6.00 33.20
        7.50 20.60
        3.30 41.30
        2.50 51.50
        6.80 28.00];

%
X = data(:,1);
Y = data(:,2);
% 进行检验
coeff = corr(X,Y,'type','Pearson');
ttest = myTtest005(coeff,length(X)-2);

%% kendall 
clc;clear;

data = [20 15 40 30 42 60 65 70 53 78;
        25 18 60 45 62 88 92 99 75 98];
data = data';
%
X = data(:,1);
Y = data(:,2);

coeff = corr(X,Y,'type','Kendall');
ttest = myTtest005(coeff,length(X)-2);

%% Spearman 
clc;clear;

data = [2 6 5 1 10 9 8 3 4 12 7 11;
        3 4 2 1 8 11 10 6 7 12 5 9];
data = data';
X = data(:,1);
Y = data(:,2);
coeff = corr(X,Y,'type','Spearman');
myTtest005(coeff,size(data,1)-2);
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t检验(t-test)临界值表-t检验表.xls

T检验代码：

myTtest005.m

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偏相关分析

%% 偏相关分析
clc;clear;

data = [429.2 16.5 32.6
        3663.8 101.4 60.0
        96.6 13.4 25.6
        289.0 32.0 9.6
        537.9 40.7 15.0
        704.0 73.5 19.4
        472.4 56.8 30.9
        1020.1 32.0 18.0
         7074.2 341.5 39.2];

%% 分析去除第二列，第一列和第三列的相关性
X = data(:,1);
Y = data(:,2);
D = data(:,3);
[coeff,p2] = partialcorr(X,Y,D);
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相关性分析及SPSS软件操作.pdf