🔆 文章首发于我的个人博客：欢迎大佬们来逛逛

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

---

双变量关系强度测量的指标

相关系数的性质

---

代码实现example

%% Pearson
clc;clear;data = [2.00 46.003.00 42.004.00 41.003.00 43.004.00 39.005.00 38.006.00 33.207.50 20.603.30 41.302.50 51.506.80 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);

t检验(t-test)临界值表-t检验表.xls

T检验代码：

myTtest005.m

---

偏相关分析

%% 偏相关分析
clc;clear;data = [429.2 16.5 32.63663.8 101.4 60.096.6 13.4 25.6289.0 32.0 9.6537.9 40.7 15.0704.0 73.5 19.4472.4 56.8 30.91020.1 32.0 18.07074.2 341.5 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);

---

相关性分析及SPSS软件操作.pdf