09本统计学区煊英43实验3报告格式.doc

实验报告课程名称 多元统计分析 实验项目名称 三、判别分析 班级与班级代码 4091501 实验室名称（或课室） 厚德楼A202 专 业 统计学 任课教师 林海明 学 号： 409150143 姓 名： 区煊英 实验日期： 2012年5月4日 广东商学院教务处 制 姓名 区煊英 实验报告成绩 评语：1.对判别分析问题的思路、理论和方法认识 正确；2.SAS软件相应计算结果确认与应用 正确； 3.SAS软件相应过程命令 正确。 指导教师（签名） 2012年5月 日说明：指导教师评分后，实验报告交院（系）办公室保存。实验项目四 判别分析实验目的：通过判别分析的实验,熟悉判别分析问题的提出、解决问题的思路、方法和技能，会调用SAS软件判别分析有关过程命令，根据计算机计算的结果，分析和解决判别分析问题。实验原理：解决判别分析问题的思路、理论和方法。实验设备：计算机与SAS软件。实验步骤：调用判别分析过程命令输入数据求得判别函数、回判结果、判别新样品类别（方法：贝叶斯判别法）。实验数据：经济全球化指标为：X1-GDP占全球GDP的比重、X2-货物贸易占货物GDP的比重、X3-外国分支机构占世界全部外国分支机构的比重、X4-本国发生的全部收益占GDP的比重、X5-本国发生的全部收益占世界发生的全部收益的比重、X6-对外直接投资和接受外国直接投资总额占GDP的比重、X7-外国直接投资占国内投资总额的比重、 X8-本国直接投资额占全球直接投资额的比重、X9-跨国并购额占全球跨国并购额的比重、X10-国际经济外向度、X11-对外贸易依存度、X12-货物和服务进出口总额占GDP的比重、X13-国际金融总资本流量占GDP的比重、X14-对外金融资产负债总额占GDP的比重、X15-国际金融总资本流量占全球国际总资本流量的比重。已分类国家及其数据：第一类样品国家及其数据：1日本、2美国、3英国、4法国、5德国1 14.079 52.3 0.653 10.254 11.769 1.097 0 1.967 1.3 6.178 14.746 27.297 30.9 57.734 15.1252 29.641 46.1 3.682 6.429 20.563 4.808 5.4 24.253 29.941 15.638 10.784 24.555 13.6 24.495 21.2843 4.67 118.4 0.497 26.151 12.456 22.137 11.2 16.552 19.642 5.542 28.434 58.7 66.1 278.968 11.2894 4.639 120.6 1.84 9.242 4.492 10.848 8.5 8.282 5.341 5.21 28.46 54.052 29.2 56.453 8.8895 6.840 132.9 2.252 9.558 6.646 7.747 2.2 3.589 8.971 8.843 32.121 63.174 36 51.514 12.18第二类样品国家及其数据：6印度、7巴西、8墨西哥、9俄罗斯、10澳大利亚、11新西兰6 1.449 31.1 0.279 0.339 0.272 0.1 2.7 0.128 0.193 0.825 2.318 5.127 0.6 1.079 0.2187 2.434 27.1 1.584 2.327 0.962 2.905 6.8 1.953 2.3 0.857 4.716 10.101 6.7 5.498 1.1048 1.567 151.4 1.657 2.837 0.797 1.471 10.9 0.67 0.212 2.186 18.485 37.986 4.5 4.887 0.4689 1.300 58.6 1.533 1.499 0.552 0.499 2.5 0.31 0.298 0.987 7.77 12.581 1.1 2.011 0.46910 1.309 94.5 0.502 5.773 0.941 1.987 18.9 0.527 1.371 1.131 15.745 33.795 13.2 24.117 0.79711 0.177 110.5 0.218 7.374 0.179 3.04 31.5 0.126 0.338 0.248 23.221 47.387 19.8 41.274 0.215新样品国家及其数据:1 中国、2韩国、3新加坡、4加拿大、5意大利。1 3.205 54.5 28.53 0.878 1.409 0.394 11.6 2.305 0.547 2.932 4.818 9.003 2.7 3.914 1.4722 1.318 136.3 1.011 1.6 0.42 1.838 1.3 0.77 0.78 2.267 23.32 42.875 9.1 12.129 0.4523 0.275 739.5 3.572 27.841 0.884 13.314 28.6 0.622 0.143 1.885 169.772 319.907 54.2 917.328 0.7184 2.056 101.5 0.898 8.276 2.313 5.369 10.5 2.444 5.145 3.854 34.691 67.047 15.1 21.83 1.362 5 3.792 104.5 0.321 8.153 3.724 1.059 2.5 0.77 1.913 4.032 22.869 43.924 27 17.776 5.678 实验结果、实验分析、结论（有关表图要有序号、中文名、表的上下线为粗线、表的内线为细线、表的左右边不封口、表图不能跨页、表图旁不能留空块, 引用结论要注明参考文献）：1、给出判别函数系数表1、判别函数；表1 两组判别函数的系数（Linear Discriminant Function for type） VariableLabel12Constant x1 x2 x3 x4 x5x6x7x8x9x10x11x12x13x14x15ABCDEFGHIJKLMNO-234.026716.065628-0.77369217.58897-23.784135.021740116.718688-2.851420.990101-12.79753-2.158915.692877-2.969472 4.8637639 0.9732735 7.2338943 -7.5698731.70110950.21460611.5895934-2.185650.47043872.40215270.05892030.4078596-1.440095-0.3386340.2259146-0.669157 1.5309011 -0.129839 0.6599562调用判别分析过程命令输入数据求得表1-4.从表1得判别函数： Y(1/x)=-234.0267+16.065628x1-0.773692x2+17.58897x3-23.78413x4+5.0217401x5+16.718688x6-2.85142x7+0.990101x8-12.79753x9-2.1589x10+15.692877x11-2.969472x12+4.8637639x13+0.9732735x14+7.2338943x15Y(2/x)=-7.569873+1.7011095x1+0.2146061x2+1.5895934x3-2.18565x4+0.4704387x5+2.4021527x6+0.0589203x7+0.4078596x8-1.440095x9-0.338634x10+0.2259146x11-0.669157x12+1.5309011x13-0.129839x14+0.6599562 x152、给出回判结果分析表2、相应回判结果分析的文字说明；表2 原各组样品回判结果（Number of Observations and Percent Classified into type） From type 1 21 2 Total5 0 5100.00 0.00 100.000 6 60.00 100.00 100.00 Total5 6 1145.45 54.55 100.00Priors0.5 0.5从表2得相应回判结果:原各组样品回判判对率为100%,判别函数效果相当好。3、给出新样品具体判别结果的表3、相应新样品具体判别结果的文字说明；从表3得新样品具体判别结果：表3 新样品判别结果与后验概率（Posterior Probability of Membership in type Classified） Obsinto type12 12345121221.00000.00001.00000.0425580.00000.00001.00000.00000.9574421.0000中国、新加坡为第1类国家；韩国、加拿大、意大利为第2类国家。第一类国家(高发展水平)均值:： 11.97380 94.06000 1.78480 12.32680 11.18520 9.32740 5.46000 10.92860 13.03900 8.28220 22.90900 45.55560 35.16000 93.83280 13.75340第二类国家(中等发展水平)均值： 1.37267 78.86667 0.96217 3.35817 0.61717 1.66700 12.21667 0.61900 0.78533 1.03900 12.04250 24.49617 7.65000 13.14433 0.54517中国：3.205 54.5 28.53 0.878 1.409 0.394 11.6 2.305 0.547 2.932 4.818 9.003 2.7 3.914 1.472第二类国家与第一类国家相比: 经济全球化的各个指标很多都是比第一类国家要低的（低的比重在于7%80%之间）、只有X3-外国分支机构占世界全部外国分支机构的比重与第一类相当接近。中国虽然判为第一类国家，但是中国的很多指标都是比第一类要低的，只是很个别的指标值比第一类标准高而已，说明中国已经顺利从第二类国家过渡到第一类国家，但是很多指标还不能达标，在以后的经济全球化中还是有很大的发展空间。4、实验程序。判别分析过程命令date fitness; input x1-x15; cards; 14.079 52.3 0.653 10.254 11.769 1.097 0 1.967 1.3 6.178 14.746 27.297 30.9 57.734 15.125 29.641 46.1 3.682 6.429 20.563 4.808 5.4 24.253 29.941 15.638 10.784 24.555 13.6 24.495 21.284 4.67 118.4 0.497 26.151 12.456 22.137 11.2 16.552 19.642 5.542 28.434 58.7 66.1 278.968 11.289 4.639 120.6 1.84 9.242 4.492 10.848 8.5 8.282 5.341 5.21 28.46 54.052 29.2 56.453 8.889 6.840 132.9 2.252 9.558 6.646 7.747 2.2 3.589 8.971 8.843 32.121 63.174 36 51.514 12.18;proc corr cov;run;date fitness; input x1-x15; cards; 1.449 31.1 0.279 0.339 0.272 0.1 2.7 0.128 0.193 0.825 2.318 5.127 0.6 1.079 0.218 2.434 27.1 1.584 2.327 0.962 2.905 6.8 1.953 2.3 0.857 4.716 10.101 6.7 5.498 1.104 1.567 151.4 1.657 2.837 0.797 1.471 10.9 0.67 0.212 2.186 18.485 37.986 4.5 4.887 0.468 1.300 58.6 1.533 1.499 0.552 0.499 2.5 0.31 0.298 0.987 7.77 12.581 1.1 2.011 0.469 1.309 94.5 0.502 5.773 0.941 1.987 18.9 0.527 1.371 1.131 15.745 33.795 13.2 24.117 0.797 0.177 110.5 0.218 7.374 0.179 3.04 31.5 0.126 0.338 0.248 23.221 47.387 19.8 41.274 0.215;proc corr cov;run;proc iml; n=5; m=6;v=(n-1)*112.51563 -376.76724 9.43450 -43.17240 57.50586 -48.44259 -15.00996 58.13975 75.75467 41.29886 -89.94027 -164.55815 -144.11239 -540.44021 49.42, -376.76724 1712.35300 -13.86493 129.12222 -199.28689 212.80290 81.16550 -120.80977 -130.49708 -97.84099 390.49950 749.84893 459.86050 1675.84887 -167.16, 9.43450 -13.86493 1.69002 -7.02750 3.25103 -4.10242 -0.67036 6.15121 8.63096 4.98955 -4.06737 -6.85107 -18.96581 -87.73572 3.72, -43.17240 129.12222 -7.02750 61.84282 -1.43384 55.12051 22.66769 11.55402 12.73486 -17.24454 30.54497 67.10576 144.94176 818.89840 -15.49, 57.50586 -199.28689 3.25103 -1.43384 38.82554 -8.47811 -0.50134 44.47453 57.50779 20.57195 -47.60482 -81.93411 -32.31882 1.56082 26.99, -48.44259 212.80290 -4.10242 55.12051 -8.47811 64.26505 32.12775 23.28696 24.88376 -14.14603 46.65943 99.65893 129.60962 748.00540 -20.45, -15.00996 81.16550 -0.67036 22.66769 -0.50134 32.12775 20.65800 24.07266 22.71045 -3.96222 15.96373 36.19848 45.32300 322.06511 -7.93, 58.13975 -120.80977 6.15121 11.55402 44.47453 23.28696 24.07266 87.58669 104.44289 26.36855 -34.90980 -48.51912 -16.37530 212.56004 24.65, 75.75467 -130.49708 8.63096 12.73486 57.50779 24.88376 22.71045 104.44289 135.72229 38.07596 -38.20893 -47.72473 -16.90980 233.54166 34.68, 41.29886 -97.84099 4.98955 -17.24454 20.57195 -14.14603 -3.96222 26.36855 38.07596 18.94926 -24.75995 -42.07425 -53.90689 -212.56938 18.33, -89.94027 390.49950 -4.06737 30.54497 -47.60482 46.65943 15.96373 -34.90980 -38.20893 -24.75995 89.96259 171.44106 110.44675 390.23281 -39.40, -164.55815 749.84893 -6.85107 67.10576 -81.93411 99.65893 36.19848 -48.51912 -47.72473 -42.07425 171.44106 332.44344 225.35080 871.38920 -71.15, -144.11239 459.86050 -18.96581 144.94176 -32.31882 129.60962 45.32300 -16.37530 -16.90980 -53.90689 110.44675 225.35080 369.12300 1891.00569 -54.19, -540.44021 1675.84887 -87.73572 818.89840 1.56082 748.00540 322.06511 212.56004 233.54166 -212.56938 390.23281 871.38920 1891.00569 10893.50661 -194.87, 49.42213 -167.16028 3.72618 -15.49081 26.99746 -20.45020 -7.93623 24.65323 34.68882 18.33062 -39.40429 -71.15859 -54.19528 -194.87521 22.70;w=(m-1)* 0.521795 -16.367553 0.324089 -1.249280 0.176520 -0.084184 -5.901413 0.401205 0.396784 0.191419 -4.111437 -8.246211 -3.312660 -8.832849 0.18, -16.367553 2375.666667 1.707527 72.451767 0.840627 13.730680 310.176667 -10.535040 -17.363867 16.061900 361.831280 751.963747 152.262000 332.044813 -4.77, 0.324089 1.707527 0.485515 -0.820339 0.143008 -0.026313 -4.150743 0.286644 0.146672 0.269323 -1.179453 -2.892204 -2.659530 -6.917455 0.12, -1.249280 72.451767 -0.820339 7.173025 -0.052727 2.402918 29.329357 -0.309447 0.209837 -0.524337 19.066463 40.565058 19.655730 41.755056 -0.02, 0.176520 0.840627 0.143008 -0.052727 0.114299 0.102134 -0.894323 0.169003 0.208216 0.119429 -0.286380 -0.418935 -0.312050 -1.600750 0.10, -0.084184 13.730680 -0.026313 2.402918 0.102134 1.475660 9.685720 0.413095 0.611629 -0.222131 5.285703 11.641583 7.319400 13.239135 0.18, -5.901413 310.176667 -4.150743 29.329357 -0.894323 9.685720 126.513667 -1.947880 -0.357827 -2.524160 81.113130 172.775317 82.283000 178.211633 -0.75, 0.401205 -10.535040 0.286644 -0.309447 0.169003 0.413095 -1.947880 0.474046 0.519880 0.063670 -1.840336 -3.492759 -0.488600 -3.226893 0.21, 0.396784 -17.363867 0.146672 0.209837 0.208216 0.611629 -0.357827 0.519880 0.750878 -0.074692 -1.956143 -3.410441 1.110080 -0.086422 0.28, 0.191419 16.061900 0.269323 -0.524337 0.119429 -0.222131 -2.524160 0.063670 -0.074692 0.406276 0.504924 1.121239 -2.138180 -5.231939 0.03, -4.111437 361.831280 -1.179453 19.066463 -0.286380 5.285703 81.113130 -1.840336 -1.956143 0.504924 69.334187 144.390312 47.915310 104.558654 -0.76, -8.246211 751.963747 -2.892204 40.565058 -0.418935 11.641583 172.775317 -3.492759 -3.410441 1.121239 144.390312 303.358159 103.102310 222.195135 -1.41, -3.312660 152.262000 -2.659530 19.655730 -0.312050 7.319400 82.283000 -0.488600 1.110080 -2.138180 47.915310 103.102310 56.371000 118.786460 -0.01, -8.832849 332.044813 -6.917455 41.755056 -1.600750 13.239135 178.211633 -3.226893 -0.086422 -5.231939 104.558654 222.195135 118.786460 261.570181 -1.07, 0.187482 -4.772413 0.120746 -0.024883 0.104588 0.187172 -0.751083 0.213065 0.283357 0.033617 -0.768568 -1.411403 -0.019250 -1.072933 0.12; z=(v+w)/(n+m-2); l=eigval(z); c=eigvec(z); h=det(z);xbar=11.9738 94.06 1.7848 12.3268 11.1852 9.3274 5.46 10.9286 13.039 8.2822 22.909 45.5556 35.16 93.8328 13.7534;ybar=1.37267 78.86667 0.96217 3.35817 0.61717 1.66700 12.21667 0.619 0.78533 1.039 12.0425 24.49617 7.65 13.14433 0.54517; A=-0.054682 -0.051089 0.4068978 0.0433486 -0.407163 0.1902225 0.1015784 0.1700966 -0.292428, 0.2992781 0.8691514 0.135096 -0.303602 -0.140217 -0.065321 0.0889454 -0.054481 0.0057783, -0.007808 0.003728 0.0501791 -0.021748 0.0765543 0.0387809 0.1672766 0.4344524 -0.164973, 0.0730645 -0.013508 -0.002145 0.0746953 -0.116028 0.0488464 0.233826 0.2270758 0.8540221, -0.00662 -0.046059 0.2681046 -0.000223 -0.298789 0.1856291 0.0472065 0.16735 -0.006521, 0.065107 -0.004287 0.0409611 -0.006277 0.4615276 -0.178531 0.5231182 0.0868356 -0.290071, 0.0572476 0.0690644 0.1934666 0.690186 -0.285862 -0.539229 0.0035523 -0.138544 -0.037051, 0.0109493 -0.048152 0.4649253 -0.002756 0.2734367 -0.26683 0.2622118 0.148889 0.1147506, 0.012276 -0.054382 0.5866762 0.0252866 0.3793331 0.2831127 -0.152427 -0.227045 0.1889365, -0.019365 -0.003131 0.199919 -0.006534 0.0117789 0.2370058 -0.15506 -0.183832 -0.078781, 0.0660857 0.1507175 -0.092916 0.2306495 0.1760916 0.0845612 -0.330968 0.7191332 -0.050651, 0.1403806 0.2940198 -0.052693 0.5130483 0.317672 0.2987743 -0.203372 -0.109766 -0.018948, 0.1761944 0.0020604 -0.242446 0.3087236 -0.081177 0.4808262 0.580139 -0.137795 -0.051957, 0.9155879 -0.346069 0.0238832 -0.101577 -0.044667 -0.057663 -0.137637 0.0089984 -0.057071, -0.02044 -0.025343 0.1777676 0.027621 -0.227873 0.2451542 0.0279865 0.1313938 -0.091508; d=5651.7929 0 0 0 0 0 0 0 0, 0 2070.0046 0 0 0 0 0 0 0, 0 0 144.2693 0 0 0 0 0 0, 0 0 0 84.687324 0 0 0 0 0, 0 0 0 0 20.939468 0 0 0 0, 0 0 0 0 0 12.417486 0 0 0, 0 0 0 0 0 0 1.0048847 0 0, 0 0 0 0 0 0 0 0.3833345 0, 0 0 0 0 0 0 0 0 0.0849791; s1=-0.78845+(-1/2)*(xbar)*A*inv(d)*A*xbar; c1=(A*inv(d)*A*xbar); s2=-0.6061+(-1/2)*(ybar)*A*inv(d)*A*ybar; c2=(A*inv(d)*A*ybar); print z; print l; print c; print A; print s1; print c1; print s2; print c2; run;proc iml;x1=1 14.079 52.3 0.653 10.254 11.769 1.097 0 1.967 1.3 6.178 14.746 27.297 30.9 57.734 15.125, 1 29.641 46.1 3.682 6.429 20.563 4.808 5.4 24.253 29.941 15.638 10.784 24.555 13.6 24.495 21.284, 1 4.67 118.4 0.497 26.151 12.456 22.137 11.2 16.552 19.642 5.542 28.434 58.7 66.1 278.968