用命令:bbin,r,rit,s]= regress(,x, alpha) 求出回归系数的点估计值和区间估计,得到回归系数并检验回归模型。命令中r和 rint分别为残差及其置信区间;α是显著水平,默认情况下α为0.05;s是用于检验回 归模型的统计量:(相关系数r2,F,与F对应的概率p),取a=005,F2(8.23)=2.37 越接近,说明回归方程越显著; 其中{F>F(n-k-1)时拒绝H0,F越大,说明回归方程越显著 P<a时拒绝H 当上述三个条件能满足时,回归模型成立。 同样还可以利用 Matlab中的函数 stool直接得到回归模型的各个检验量 回归系数的点估计值及其置信区间 bint 110296501037571127311312289130414913204291|-1088732-1097130,108034 082810081008461 0.05610.0530 [0.0443.,0.0522] 0.1279 [0.12430.1314] 0.0616 [0.05800.0653 05301005160054 0000000012.0011 [-0.1578.0.1554 0.11990.11680.1230] 0.0333 [0.03050.0360] -0.0099 [-0.0127,0.0071 00254-00274-00235 0.1245 [0.12270.1263] 0.12200.194..246 0.1124 0.1148-0.1101 0.0021 0.12160.11850.1246 -0.0189 0.0216-0.0162 0.0025 0.0053,0.0003] 0.0012[00034,0.0009 0.0987 [0.0968,0.1006] 0.2014 -0.2033,-0.1994 7481776673478290132974532137,3818】120603197094121617 -0.0345 -0.0360 -0.1024 -0.1059, ,-0.03291 0.0005 -0.00110.0021 0.2378 [0.23600.2396 0.2433 0.23970.2469] -0.0643.0.0561] 0.2052 [0.20400.2063] -00646 -0.0658,0.0633] -0.0779 -0.0793.0.0765] -00235,0.0181] -0.0411 -0.0439,-0.0383 0.0930 -0.0118 -0.0135,0.0101 -0.0652 -0.0670,0.0635 0.0469 [0.04490.0489 0.0060 [0.00370.0082] [0.06800.0727] 0.0001 -0.0026,0.0028] 0.1449 [0.14230.1476 -0.0070,0.0015] 0.1659 [0.16280.1691 0.076 0.0747,00784 0108,-0.0070 0.0007 -00015,0.0029 这样就得到了各条线路的回归方程如下 l=110.2965+0.0828x1+0.0483x2+0.0530x3+0.11994-0.0254X5+0.1220x6+0.1216x7 -0.0012x8用命令：[ ]srint,r,bint,b, = ( ,, alphaxyregress ) 求出回归系数的点估计值和区间估计，得到回归系数并检验回归模型。命令中r 和 rint 分别为残差及其置信区间；α 是显著水平，默认情况下α 为 0.05；s 是用于检验回 归模型的统计量： ( 相关系数 2 α ,, 与FFr 对应的概率 p) ，取α =0.05， ( 23,8 =2.37 Fα ) 其中 ( ) ⎪ ⎩ ⎪ ⎨ ⎧ < −−> 0 0 2 , 1, 1 p H knkFF FH r 时拒绝 时拒绝 越大，说明回归方程越显著； 越接近 ，说明回归方程越显著； α α 当上述三个条件能满足时，回归模型成立。 同样还可以利用 Matlab 中的函数 rstool 直接得到回归模型的各个检验量。 回归系数的点估计值及其置信区间： b1 bint1 b2 bint2 b3 bint3 110.2965 [109.3757,111.2173] 131.2289 [130.4149,132.0429] -108.8732 [-109.7130,-108.0334 ] 0.0828 [0.0811,0.0846] -0.0546 [-0.0561,-0.0530] -0.0695 [-0.0711,-0.0679] 0.0483 [0.0443,0.0522] 0.1279 [0.1243,0.1314] 0.0616 [0.0580,0.0653] 0.0530 [0.0516,0.0543] 0.0000 [-0.0012,0.0011] -0.1566 [-0.1578,-0.1554] 0.1199 [0.1168,0.1230] 0.0333 [0.0305,0.0360] -0.0099 [-0.0127,-0.0071] -0.0254 [-0.0274,-0.0235] 0.0868 [0.0851,0.0886] 0.1245 [0.1227,0.1263] 0.1220 [0.1194,0.1246] -0.1124 [-0.1148,-0.1101] 0.0021 [-0.0003,0.0045] 0.1216 [0.1185,0.1246] -0.0189 [-0.0216,-0.0162] -0.0025 [-0.0053,0.0003] -0.0012 [-0.0034,0.0009] 0.0987 [0.0968,0.1006] -0.2014 [-0.2033,-0.1994] b4 bint4 b5 bint5 b6 bint6 77.4817 [76.6734,78.2900] 132.9745 [132.1371,133.8118] 120.6633 [119.7094,121.6171] -0.0345 [-0.0360,-0.0329] 0.0005 [-0.0011,0.0021] 0.2378 [0.2360,0.2396] -0.1024 [-0.1059,-0.0989] 0.2433 [0.2397,0.2469] -0.0602 [-0.0643,-0.0561] 0.2052 [0.2040,0.2063] -0.0646 [-0.0658,-0.0633] -0.0779 [-0.0793,-0.0765] -0.0208 [-0.0235,-0.0181] -0.0411 [-0.0439,-0.0383] 0.0930 [0.0898,0.0962] -0.0118 [-0.0135,-0.0101] -0.0652 [-0.0670,-0.0635] 0.0469 [0.0449,0.0489] 0.0060 [0.0037,0.0082] 0.0703 [0.0680,0.0727] 0.0001 [-0.0026,0.0028] 0.1449 [0.1423,0.1476] -0.0043 [-0.0070,-0.0015] 0.1659 [0.1628,0.1691] 0.0765 [0.0747,0.0784] -0.0089 [-0.0108,-0.0070] 0.0007 [-0.0015,0.0029] 这样就得到了各条线路的回归方程如下： y1=110.2965+0.0828x1+0.0483x2+0.0530x3+0.1199x4-0.0254x5+0.1220x6+0.1216x7 -0.0012x8 6