试验设计与数据处理第二版课后习题答案.xls

试验设计与数据处理第二版课后习题答案.xls

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试试验验设设计计与与数数据据处处理理课课后后习习题题答答案案第二章习题答案2.10.04150.03550.03360.03120.03020.02890.0277Re175002550029700376004240050900618002.2x1234Mx=1/x=20mmy88.28.38My=1/y=5mm关系曲线0.10.01100001000000Rex与y的关系图4.543.532.521.510.507.87.988.18.28.38.4xy系列12.3发酵时间/d012345PH值5.45.865.95.85.7残糖量/（g/l）24.513.311.210.19.58.12.4树脂型号DA-201NKA-9AB-8D-4006D-101S-8NKA-吸附量/（mg/g）17.1417.771.8713.710.5513.333.67发酵时间与PH值及残糖量的关系图98765432100510152025304.95.15.35.55.75.96.1发酵时间/dph值残糖量PH值DA-201NKA-9AB-8D-4006D-101S-8NKA-02468101214161820树脂型号吸附量/（mg/g）x与y的关系图4.543.532.521.510.507.87.988.18.28.38.4xy系列12.5植物油凝固点/甲乙花生油2.93.5棉子油-6.3-6.2蓖麻油-0.10.5菜籽油5.352.6应用领域橡胶工业合成表面活性剂润滑油（脂）肥皂及洗涤剂金属皂其他比例/%181152321222.9-6.3-0.15.33.5-6.20.55两个产地几种植物油的凝固点数据图花生油棉子油蓖麻油菜籽油-7-6-5-4-3-2-10123456植物油凝固点/甲乙18115232122比例/%橡胶工业合成表面活性剂润滑油（脂）肥皂及洗涤剂金属皂其他DA-201NKA-9AB-8D-4006D-101S-8NKA-02468101214161820树脂型号吸附量/（mg/g）3.1第三章习题答案3.1颜色销售额/万元橘黄色26.528.725.129.127.2粉色31.228.330.827.929.6绿色27.925.128.524.226.5无色30.829.632.431.732.8方差分析：

单因素方差分析SUMMARY组观测数求和平均方差行15136.627.322.672行25147.829.562.143行35132.226.443.298行45157.331.461.658方差分析差异源SSdfMSFP-valueFcrit组间76.8455325.6151710.48620.0004663.238872组内39.084162.44275总计115.9295193.2乙炔流量空气流量/（L/min）/（L/min）89101112181.181.580.380771.581.481.879.479.175.927576.175.475.470.818115232122比例/%橡胶工业合成表面活性剂润滑油（脂）肥皂及洗涤剂金属皂其他2.560.467.968.769.868.7方差分析：

无重复双因素分析SUMMARY观测数求和平均方差行15399.979.983.137行25397.679.525.507行35372.774.544.528行45335.567.114.485列14297.974.47596.7425列24307.376.82542.2625列34303.875.9527.89667列44304.376.07521.4625列54292.473.115.9方差分析差异源SSdfMSFP-valueFcrit行537.63753179.212528.614869.44E-063.490295列35.47348.868251.4159940.2874223.259167误差75.155126.262917总计648.2655193.3铝材材质去离子水自来水12.35.611.85.321.55.321.54.831.87.432.37.4方差分析：

可重复双因素分析SUMMARY去离子水自来水总计1观测数224求和4.110.915平均2.055.453.75方差0.1250.0453.912观测数224求和310.113.1平均1.55.053.275方差00.1254.24253观测数224求和4.114.818.9平均2.057.44.725方差0.12509.5825总计观测数66求和11.235.8平均1.8666666675.966667方差0.1306666671.298667方差分析差异源SSdfMSFP-valueFcrit样本4.37166666722.18583331.226190.0006735.143253列50.43150.43720.42861.77E-075.987378交互2.35521.177516.821430.0034675.143253内部0.4260.07总计57.57666667114.1c/%（x）T/（y）19.6105.420.510622.3107.225.1108.926.3109.627.8110.729.1111.5浓度与沸点温度之间的关系3129272523211917102104106108110112c/%T/系列1ixyxi2yi2xiyi119.6105.4384.1611109.162065.84220.5106420.25112362173322.3107.2497.2911491.842390.56425.1108.9630.0111859.212733.39526.3109.6691.6912012.162882.48627.8110.7772.8412254.493077.46729.1111.5846.8112432.253244.65总和170.7759.34243.0582395.1118567.38平均24.38571108.4714286SUMMARYOUTPUT回归统计MultipleR0.999752712RSquare0.999505486AdjustedRSquare0.999406583标准误差0.056916528观测值7方差分析dfSSMSFSignificanceF回归分析132.7380932.7380910105.941.85E-09残差50.0161970.003239总计632.75429Coefficients标准误差tStatP-valueLower95%Upper95%Intercept92.911379380.156271594.55442.55E-1392.5096793.31309XVariable10.6380805170.006347100.52831.85E-090.6217640.654397浓度与沸点温度之间的关系3129272523211917102104106108110112c/%T/系列14.2T/Kc/%lnTlnc273202.4361631.30103SUMMARYOUTPUT283252.4517861.39794293312.4668681.491362回归统计313342.4955441.531479MultipleR0.987715333462.5224441.662758RSquare0.97558353582.5477751.763428AdjustedRSquare0.969475标准误差0.029578观测值6方差分析df回归分析1残差4总计5CoefficientsIntercept-8.1419XVariable13.887206SUMMARYOUTPUT回归统计MultipleR0.987714594RSquare0.97558012AdjustedRSquare0.969475149标准误差0.029578225观测值6方差分析dfSSMSFSignificanceF回归分析10.1398050.139805159.8010.000225残差40.0034990.000875总计50.143305Coefficients标准误差tStatP-valueLower95%Upper95%Intercept-8.1418961590.76478-10.64610.000441-10.2653-6.01853XVariable13.887206360.30750212.641240.0002253.0334444.7409690.000291某物质的溶解度与绝对温度之间的关系100010010100T/Kc/系列13.8872064.3试验号煎煮时间/min（x1）煎煮次数（x2）加水量/倍（x3）含量/（mg/L）y1301815240211373503746460110265702634680395779031257SUMMARYOUTPUT回归统计MultipleR0.992299718RSquare0.984658731AdjustedRSquare0.969317462标准误差2.742554455观测值7方差分析dfSSMSFSignificanceF回归分析31448.292482.764164.183660.003211残差322.564817.521605总计61470.857Coefficients标准误差tStatP-valueLower95%Upper95%Intercept-12.611111115.352918-2.355930.099767-29.64654.424264XVariable10.1750.0669112.6153960.079315-0.037940.387942XVariable213.712962961.5612688.7832230.0031098.74431218.68161XVariable31.2870370370.5371472.396060.096215-0.422412.9964794.4试验号T/Na2O（x1）/%siO2（x2）/%CaO（x3）/%X1=x1X2=x1x21102914729.11410082101114728.11410083101614727.1141008410061473.38.8141026.259931473.36.8141026.2610041473.38.1141026.279671473.37.1141026.289991473.36.1141026.299921474.37.8141040.21098014747.11410361198014746.11410361298414747.11410361396515716.115106514100615719.11510651598815727.11510801698415729.11510801796715728.11510801898715727.11510801997915728.11510802098815726.11510802196815738.11510952294015737.11510952395615736.11510952495615738.11510952592515736.1151095SUMMARYOUTPUT回归统计MultipleR0.866175908RSquare0.750260704AdjustedRSquare0.714583661标准误差12.79120464观测值25方差分析dfSSMSFSignificanceF回归分析310322.093440.69621.029231.57E-06残差213435.913163.6149总计2413758Coefficients标准误差tStatP-valueLower95%Upper95%Intercept1557.05891196.9955616.052892.89E-131355.3461758.772XVariable138.6453237415.362782.5155160.0200936.69666670.59398XVariable2-1.1212663080.249355-4.496660.000198-1.63983-0.6027XVariable36.4842365252.6881352.4121690.025090.89395412.07452所以得到的线性回归方程表达式为：

y=1557.06+38.65x1-1.12x1x2+6.48x3根据偏回归系数的大小，可知三个因素的主次顺序为：

x1x3x2。

5.1优选过程：

1、首先在试验范围0.618处做第一个实验，这一点的温度为：

x1=340+（420-340）0.618=389.44.2、在这点的对称点，即0.382处做一个实验，这一点的温度为：

x1=420-（420-340）0.618=370.56.3、比较两次的实验结果，发现第一点比第二点的合成率高，故舍去370.56以下部分，在370.56-420之间，找x1的对称：

x3=420-（420-370.56）0.618=389.44608.4、比较两次的实验结果，发现第一点比第三点的合成率高，故舍去389.44608以下部分，在389.44608-420之间，找x1的对称：

x4=420-（420-389.44608）0.618=401.11767744.5、比较两次的实验结果，发现第一点比第四点的合成率高，故舍去401.11767744以上部分，在389.44608-401.11767744之间，找x1的对称：

x5=401.11767744-（401.11767744-389.44608）0.618=393.787.5.2电解质温度657480电解率94.398.981.5目标函数101.4993x=70.62664887则下一个实验点为70.63。

5.4黄金分割法首先在实验范围的0.618处做第一个实验，这一点的碱液用量为9080706050403020100020406080100120y=-0.2274x2+32.1207x-1032.7519电解质温度电解率）（）（）（）（）（）（212131323212221321232232214xxyxxyxxyxxyxxyxxyxx1=20+（80-20）*0.618=57.08（ml）在这一点的对称点，即0.382处做第二个实验，这一点的碱液用量为x2=80-（80-20）*0.618=42.92（ml）比较两次试验结果，第二点较第一点好，则去掉57.08以上的部分，然后在20ml与57.08ml之间，找x2的对称点x3=57.08-（57.08-20）*0.618=34.165（ml）比较第二点与第三点，第二点较好，则去掉34.165以下的部分，然后在34.165ml与57.08ml之间，找x2的对称点x4=34.165+（57.08-34.165）*0.618=48.326（ml）比较第二点与第四点，第四点较好，则去掉42.29以下的部分，然后在42.29ml与57.08ml之间，找x4的对称点x5=42.29+（57.08-42.29）*0.618=51.43（ml）由于x5属于50ml到55ml之间，则为最佳点。

5.5对开法在直角坐标系中画出一矩形代表优选范围：

20x100,30y160.在中线x=（20+100）/2=60上用单因素法找到最大值，设最大值在P点。

再在中线y=（30+160）/2=90上用单因素法找到最大值，设最大值在Q点。

比较P和Q的结果，如果Q大，去掉xx2x3x4又x3x4对应的偏回归系数不显著，故归入残差项，重新进行回归分析如下：

SUMMARYOUTPUT回归统计MultipleR0.986424302RSquare0.973032904AdjustedRSquare0.964043872标准误差2.046677524观测值9方差分析dfSSMSFSignificanceF回归分析2906.8667453.4333108.24671.96E-05残差625.133334.188889总计8932Coefficients标准误差tStatP-valueLower95%Upper95%Intercept20.393333332.5497367.9982130.00020414.1543526.63231丙烯酸用量x1/mL1.720.1220414.093717.97E-061.4213782.018622引发剂用量x2/%-10.333333333.051007-3.386860.014733-17.7989-2.86779简化后的方程非常显著，两偏回归系数也都显著，所以得到最终的二元线性方程：

y=y=18.585+1.644x1-11.667x27.2序号废弃塑料质量x1/kg改性剂用量x2/kg增塑剂用量x3/kg混合剂用量x4/kgx1x2x3x3114712589814421610186816032431813556234254205146610019652281954176361624118642646472615165239025682862062168400930910502701001032121760384289SUMMARYOUTPUT回归统计MultipleR0.997692974RSquare0.995391271AdjustedRSquare0.986173814标准误差3.039793997观测值10方差分析dfSSMSFSignificanceF回归分析65987.179997.8632107.98980.001361残差327.721049.240348总计96014.9Coefficients标准误差tStatP-valueLower95%Upper95%Intercept275.851306146.783055.8963940.009738126.9668424.7359废弃塑料质量x1/kg-9.1640495881.288022-7.114820.005714-13.2631-5.06499改性剂用量x2/kg-21.903245943.378473-6.483180.007449-32.6551-11.1514增塑剂用量x3/kg-21.14261092.603757-8.120040.003905-29.4289-12.8563混合剂用量x4/kg1.4028777920.1910337.3436440.0052180.7949262.01083x1x21.164586030.1381498.4299190.0035030.7249341.604238x3x30.7275238170.0975817.455620.0049960.4169791.038069回归方程：

y=275.851-9.164x1-21.903x2-21.143x3+1.403x4+1.16x1x2+0.73x32因素主次x1x2x3x4x1x2方程非常显著，偏回归系数也非常显著，所以四个因素对试验结果都有非常显著地影响。

下面用规划求解来求得最大值x114127.179x25x35x468第八章习题答案8.1试验号z1z2z1z2z3z1z3灰化温度x1/1111117002111-1-170031-1-11170041-1-1-1-17005-11-11-13006-11-1-113007-1-111-13008-1-11-11300SUMMARYOUTPUT回归统计MultipleR0.994230144RSquare0.988493579AdjustedRSquare0.959727528标准误差0.007905694观测值8方差分析dfSSMSFSignificanceFz110.0007610.0007619.51E-05z210.0091130.0091130.001139z310.0001810.0001812.26E-05z1z210.0002640.0002643.31E-05z1z310.000420.000425.26E-05回归分析50.0107390.00214834.36320.028518残差20.0001256.25E-05总计70.010864Coefficients标准误差tStatP-valueLower95%Upper95%Intercept0.504750.002795180.58483.07E-050.4927240.516776z10.009750.0027953.4882660.073266-0.002280.021776z20.033750.00279512.074770.0067890.0217240.045776z1z20.004750.0027951.6994120.231342-0.007280.016776z3-0.005750.002795-2.057180.175939-0.017780.006276z1z30.007250.0027952.5938390.122018-0.004780.019276回归方程：

y=0.50475+0.00975z1+0.03375z2+0.00475z1z2-0.00575z3+0.00725z1z3由该回归方程中偏回归系数绝对值的大小，可以得到各因素和交互作用的主次顺序为：

由方差分析的结果可知，只有z2因素对试验指标有非常显著的影响，故可把其他因素归入残差项，重新进行方差分析得到如下表：

第二次方差分析表dfSSMSF显著性z210.0091130.00911333.62546*残差e60.0016260.000271总计70.010864因素z2对试验指标y有非常显著的影响，因此回归方程可以简化为：

y=0.50475+0.03375z2又z2=（x2-2100）/300,回带得方程y=0.2685+0.0001125x28.2试验号z1z2z3提取率y/%11118211-17.331-116.941-1-16.45-1116.96-11-16.57-1-1168-1-1-15.190006.6100006.5110006.6SUMMARYOUTPUT回归统计MultipleR0.990265441RSquare0.980625644AdjustedRSquare0.972322348标准误差0.121074733观测值11方差分析dfSSMSFSignificanceFz112.101252.10125#DIV/0!

z212.311252.31125#DIV/0!

z310.781250.78125#DIV/0!

回归分析35.193751.73125118.10082.34E-06残差70.1026140.014659总计105.296364Coefficients标准误差tStatP-valueLower95%Upper95%Intercept6.6181818180.036505181.29324.1E-146.531866.