模型代码如下：
function [b,bint,r,rint,states,sima2,p,y0,zxqj]=huigui(x,y,x0)%x 一元线性模型自变量的n个观测值的（n*p）矩阵；y 一元线性模型因变量的n个观测值的n×1向量，x0为预测值的横坐标b；%b 回归系数的最小二乘估计值，bint 估计值b的置信区间，r 模型拟合残差，rint -模型拟合残差的置信区间；%stats 有三个数值包含R^2统计量、方差分析的F统计量的值、方差分析的显著性概率p值和sigama^2的估计值；输出的y0为预测值纵坐标format short;x1=[ones(length(x),1),x];[b,bint,r,rint,states]=regress(y,x1);sima2=(vpa(states(4),20));p=vpa(states(3),20); %检验的p值 p<0.01,回归方程高度显著;0.0.1<=p<0.05,回归方程显著;p>=0.05,回归方程不显著y0=b(1)+b(2)*x0;s=sqrt(states(4));zxqj=[y0-2*s,y0+2*s]; %置信区间plot(x,y,'.'),lsline%rcoplot(r,rint) %残差分析
结果：
>> x=[1,2,3,4,5,6,7,8,9]';>> y=[2,4,6,8,10,11,14,16,18]';>> x0=10;>> [b,bint,r,rint,states,sima2,p,y0,zxqj]=huigui(x,y,x0)b =-0.02781.9833bint =-0.6342 0.57861.8756 2.0911r =0.04440.06110.07780.09440.1111-0.87220.14440.16110.1778rint =-0.6654 0.7543-0.7116 0.8338-0.7363 0.8918-0.7426 0.9315-0.7321 0.9543-0.8722 -0.8722-0.6611 0.9500-0.5981 0.9203-0.5124 0.8679states =1.0e+03 *0.0010 1.8941 0.0000 0.0001sima2 =0.12460317460317460317p =0.00000000088276169535500757861y0 =19.8056zxqj =19.0996 20.5115
