数学建模A题答案新.doc

A题 葡萄酒的评价确定葡萄酒质量时一般是通过聘请一批有资质的评酒员进行品评。每个评酒员在对葡萄酒进行品尝后对其分类指标打分，然后求和得到其总分，从而确定葡萄酒的质量。酿酒葡萄的好坏与所酿葡萄酒的质量有直接的关系，葡萄酒和酿酒葡萄检测的理化指标会在一定程度上反映葡萄酒和葡萄的质量。附件1给出了某一年份一些葡萄酒的评价结果，附件2和附件3分别给出了该年份这些葡萄酒的和酿酒葡萄的成分数据。请尝试建立数学模型讨论下列问题：1. 分析附件1中两组评酒员的评价结果有无显著性差异，哪一组结果更可信？2. 根据酿酒葡萄的理化指标和葡萄酒的质量对这些酿酒葡萄进行分级。3. 分析酿酒葡萄与葡萄酒的理化指标之间的联系。4分析酿酒葡萄和葡萄酒的理化指标对葡萄酒质量的影响，并论证能否用葡萄和葡萄酒的理化指标来评价葡萄酒的质量？附件1：葡萄酒品尝评分表（含4个表格）附件2：葡萄和葡萄酒的理化指标（含2个表格）附件3：葡萄和葡萄酒的芳香物质（含4个表格）图1两组红葡萄酒的平均值、和标准差第一组红葡萄酒第二组红葡萄酒平均值标准差平均值标准差酒样品162.79.638465酒样品168.19.048634酒样品280.36.307843酒样品2744.027682酒样品380.46.769211酒样品374.65.541761酒样品468.610.39444酒样品471.26.425643酒样品573.37.874713酒样品572.13.695342酒样品672.27.728734酒样品666.34.595892酒样品771.510.17895酒样品765.37.91693酒样品872.36.634087酒样品8668.069146酒样品981.55.739725酒样品978.25.072803酒样品1074.25.51362酒样品1068.86.014797酒样品1161.77.91693酒样品1161.66.168018酒样品1253.98.924996酒样品1268.35.012207酒样品1374.66.703233酒样品1368.83.910101酒样品14736酒样品1472.64.812022酒样品1558.79.250225酒样品1565.76.429965酒样品1674.94.254409酒样品1669.94.483302酒样品1779.39.381424酒样品1774.53.02765酒样品1859.96.871034酒样品1865.47.089899酒样品1969.46.25744酒样品1972.67.426679酒样品2078.65.103376酒样品2075.86.250333酒样品2177.110.77497酒样品2172.25.95912酒样品2277.27.11493酒样品2271.64.926121酒样品2385.65.699903酒样品2377.14.976612酒样品24788.653837酒样品2471.53.27448酒样品2569.28.038795酒样品2568.26.613118酒样品2673.85.593647酒样品26726.44636酒样品27737.055337酒样品2771.54.527693图2两组白葡萄酒的平均值、和标准差第一组白葡萄酒第二组白葡萄酒干白品种平均值标准差干白品种平均值标准差酒样品1829.60324酒样品177.95.087021酒样品274.214.1798酒样品275.87.00476酒样品385.319.10817酒样品375.611.93687酒样品479.46.686637酒样品476.96.488451酒样品57111.24475酒样品526.15.126185酒样品668.412.75583酒样品675.54.766783酒样品777.56.258328酒样品774.21.212265酒样品871.413.54991酒样品872.35.578729酒样品972.99.631545酒样品980.410.30857酒样品1074.314.58348酒样品1079.88.390471酒样品1172.313.30873酒样品1171.49.371351酒样品1263.310.76052酒样品1272.411.83404酒样品1365.913.06777酒样品1373.96.838616酒样品147210.68748酒样品1477.13.984693酒样品1572.411.4717酒样品1578.47.351493酒样品167413.34166酒样品1653.19.06826酒样品1778.812.00741酒样品1780.36.201254酒样品1873.112.51177酒样品1876.75.498485酒样品1972.26.811755酒样品1976.45.103376酒样品2077.88.024961酒样品2043.27.07421酒样品2176.413.14196酒样品2179.28.024961酒样品227111.77568酒样品2279.47.321202酒样品2375.96.607235酒样品2377.43.405877酒样品2473.310.54145酒样品2476.16.208417酒样品2577.15.820462酒样品2579.510.31988酒样品2681.38.53815酒样品2674.37.532168酒样品2764.812.01666酒样品27775.962848酒样品2881.38.969702酒样品2879.65.037636描述统计量N均值标准差方差统计量统计量标准误统计量统计量VAR000032768.51851.507227.8317461.336VAR000042774.44442.2420111.64980135.718VAR000052772.70372.7026514.04338197.217VAR000062765.29631.443937.5029056.293VAR000072774.18522.6446913.74223188.849VAR000082772.70372.1309111.07254122.601VAR000092771.22221.510027.8462861.564VAR000102772.07411.9545610.15619103.148VAR000112778.44441.230356.3931140.872VAR000120Zscore(VAR00003)0Zscore(VAR00004)0Zscore(VAR00005)0Zscore(VAR00006)0Zscore(VAR00007)0Zscore(VAR00008)0Zscore(VAR00009)0Zscore(VAR00010)0Zscore(VAR00011)0Zscore(VAR00012)0有效的 N （列表状态）0模型描述模型名称MOD_2因变量1VAR000032VAR000073VAR000054VAR000115VAR00008方程1二次自变量VAR00004常数包含其值在图中标记为观测值的变量未指定用于在方程中输入项的容差.0001个案处理摘要N个案总数27已排除的个案a0已预测的个案0新创建的个案0a. 从分析中排除任何变量中带有缺失值的个案。变量处理摘要变量因变量自变量VAR00003VAR00007VAR00005VAR00011VAR00008VAR00004正值数272727272727零的个数000000负值数000000缺失值数用户自定义缺失000000系统缺失000000模型汇总和参数估计值因变量:VAR00003方程模型汇总参数估计值R 方Fdf1df2Sig.常数b1b2二次.4278.956224.00183.975-1.026.011自变量为 VAR00004。模型描述模型名称MOD_2因变量1VAR000032VAR000073VAR000054VAR000115VAR00008方程1二次自变量VAR00004常数包含其值在图中标记为观测值的变量未指定用于在方程中输入项的容差.0001个案处理摘要N个案总数27已排除的个案a0已预测的个案0新创建的个案0a. 从分析中排除任何变量中带有缺失值的个案。变量处理摘要变量因变量自变量VAR00003VAR00007VAR00005VAR00011VAR00008VAR00004正值数272727272727零的个数000000负值数000000缺失值数用户自定义缺失000000系统缺失000000模型汇总和参数估计值因变量:VAR00003方程模型汇总参数估计值R 方Fdf1df2Sig.常数b1b2二次.4278.956224.00183.975-1.026.011自变量为 VAR00004。模型描述模型名称MOD_2因变量1VAR000032VAR000073VAR000054VAR000115VAR00008方程1二次自变量VAR00004常数包含其值在图中标记为观测值的变量未指定用于在方程中输入项的容差.0001个案处理摘要N个案总数27已排除的个案a0已预测的个案0新创建的个案0a. 从分析中排除任何变量中带有缺失值的个案。变量处理摘要变量因变量自变量VAR00003VAR00007VAR00005VAR00011VAR00008VAR00004正值数272727272727零的个数000000负值数000000缺失值数用户自定义缺失000000系统缺失000000模型汇总和参数估