各位高人:本人现在有两组自变量X和R,每组自变量有3个备选自变量X1、X2、X3以及R1、R2、R3。现在想从每组自变量中选取一个变量做回归,需要做出很多的多元回归模型,最后选择一个最优的回归模型。请问该如何编程,因为本人刚接触SAS,请高人帮忙,谢谢!!!
(事实上,备选自变量更多,我就以这个为例吧)
数据如下:
| y(因变量) | X1 | X2 | X3 | R1 | R2 | R3 |
0.053512831 | 36787.89 | 201708.14 | 29.51 | 0.5448 | 1.7736 | 2.0016 |
0.436636199 | 38246.97 | 221445.81 | 32.35 | 0.5652 | 1.8473 | 2.0026 |
-0.009960103 | 39080.58 | 229397.93 | 29.94 | 0.404 | 1.5324 | 1.5044 |
0.040347052 | 38904.85 | 240580 | 24.56 | 0.8854 | 2.3058 | 3.2539 |
0.347239889 | 41854.41 | 243821.9 | 20.87 | 0.5771 | 2.1079 | 1.9134 |
0.294396789 | 44628.17 | 266621.54 | 21.19 | 0.8442 | 2.7495 | 2.6993 |
-0.007090292 | 44845.22 | 266255.48 | 15 | 1.0052 | 2.7672 | 2.6239 |
-2.756333925 | 44477.8 | 274662.57 | 13.1 | 1.7002 | 2.3788 | 6.1126 |
-0.207187766 | 50748.46 | 289847.7 | 7.9 | 1.3099 | 1.1715 | 5.2577 |
0.231135137 | 49284.64 | 287526.17 | 4.7 | 2.1676 | 1.7262 | 11.9007 |
0.273730023 | 54659.77 | 308664.23 | 6.5 | 2.877 | 3.1508 | 16.4656 |
-0.016245517 | 55460.52 | 310898.29 | 11.9 | 1.24 | 1.1932 | 7.1747 |
-0.117192584 | 54063.91 | 313499.82 | 9.1 | 2.4496 | 2.7486 | 14.6027 |
0.162745971 | 56492.53 | 312330.34 | 8.9 | 2.5517 | 3.3287 | 15.3785 |
-0.135014223 | 58574.44 | 337291.05 | 9.3 | 3.1358 | 3.9195 | 19.7635 |
-0.147120947 | 58329.3 | 327683.74 | 5.4 | 0.5448 | 0.3484 | 3.6143 |
-0.01030496 | 36787.89 | 201708.14 | 29.51 | 3.6107 | 6.0881 | 16.4323 |
-0.025558316 | 38246.97 | 221445.81 | 32.35 | 0.8316 | 3.4968 | 4.4755 |
-0.039304509 | 39080.58 | 229397.93 | 29.94 | -6.3568 | -4.2724 | -76.4189 |
0.055595692 | 38904.85 | 240580 | 24.56 | -0.5796 | 1.839 | -4.4034 |
0.100514905 | 41854.41 | 243821.9 | 20.87 | 4.8836 | 7.3595 | 22.1667 |
0.032450952 | 44628.17 | 266621.54 | 21.19 | 0.5609 | 3.1777 | 2.7753 |
-0.004289522 | 44845.22 | 266255.48 | 15 | -6.204 | -3.7892 | -60.4365 |
0.051862107 | 44477.8 | 274662.57 | 13.1 | -2.5156 | 0.0208 | -15.3524 |
0.026745104 | 50748.46 | 289847.7 | 7.9 | -0.4748 | 2.3313 | -1.8062 |
0.103358936 | 49595.74 | 277998.11 | 4.4 | -3.69 | -1.44 | -25.5257 |
0.013302743 | 49284.64 | 287526.17 | 4.7 | -1.221 | 0.948 | -5.6106 |
0.125716971 | 53433.49 | 286788.21 | 7.3 | 8.6437 | 12.5563 | 22.6234 |
