regress线性回归

1、课本第10章10.1牙膏的销售量x1=-0.050.250.6000.250.200.150.05-0.150.150.200.100.400.450.350.300.500.500.40-0.05-0.05-0.100.200.100.500.60-0.0500.050.55;x2=5.506.757.255.507.006.506.755.255.256.006.506.257.006.906.806.807.107.006.806.506.256.006.507.006.806.806.505.755.806.80;y=7.388.519.527.509.338.288.757.877.

2、108.007.898.159.108.868.908.879.269.008.757.957.657.278.008.508.759.218.277.677.939.26;scatter(x1,y,'ro')scatter(x2,y,'ro')(发现第一个符合正比例关系，第二个基本符合二次函数关系)输入：x=ones(30,1),x1',x2',(x2).A2'b,bint,r,rint,stats=regress(y',x)得到：系数参数估计值b=17.32441.3070-3.69560.3486系数参数估计值的置信区间bi

3、nt=5.728228.92060.68291.9311-7.49890.1077(包括0点)0.03790.6594残差向量r=-0.0988-0.0795-0.1195-0.04410.4660-0.01330.29120.2735-0.23510.1031-0.40330.17470.0400-0.15040.12840.1637-0.0527-0.1907-0.0870-0.0165-0.1292-0.3002-0.2933-0.1679-0.21770.11160.30350.06930.24740.2270r残差向量的置信区间rint=-0.52780.3302-0.53180.3

4、727-0.51130.2724-0.47390.38570.08060.8515-0.46170.4351-0.13820.7206-0.0877-0.5967-0.3288-0.8199-0.2626-0.4041-0.5941-0.3216-0.2850-0.4839-0.6257-0.5357-0.4432-0.5617-0.7189-0.7252-0.5556-0.6457-0.3002-0.1045-0.3722-0.1815-0.18990.63470.12650.53500.01330.61200.48400.29340.57840.61250.37840.24420.3617

5、0.41010.30320.11860.13860.21970.21030.52340.71140.51080.67630.6438回归模型的检验统计量stats=0.905482.94090.00000.0490RA2(相关系数)Fp<0.000sA2去掉x2的系数，保留x2平方的系数。模型改进x=ones(30,1),x1',x2',(x2).A2',(x1.*x2)'b,bint,r,rint,stats=regress(y',x)得到：b=29.113311.1342-7.60800.6712-1.4777bint=13.701344.52

6、521.977820.2906-12.6932-2.52280.25381.0887-2.8518-0.1037r=-0.0441-0.12290.0299-0.07450.3841-0.04720.23310.0287-0.06610.0297-0.43720.17630.0356-0.13820.10270.12700.0048-0.1435-0.10160.0050-0.0389-0.1334-0.3272-0.3274-0.21020.14120.32500.10960.23420.2455rint=-0.44330.3551-0.54170.2960-0.31080.3706-0.4

7、7450.32550.02370.7445-0.46490.3704-0.16830.6345-0.23750.2949-0.37580.2436-0.36990.4293-0.8126-0.0619-0.23140.5840-0.37970.4508-0.55300.2766-0.31810.5235-0.29250.5465-0.39520.4047-0.54980.2629-0.52020.3169-0.39340.4035-0.43690.3591-0.50530.2386-0.72200.0675-0.6333-0.0215-0.60940.1890-0.24060.5231-0.04920.6992-0.29970.5190-0.16590.6343-0.13990.6310stats=0.920972.77710.00000.0426yi=1.52915*x1+8.0195