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The evaluation of wineAbstract: Using nonparametric statistical model and statistical regression model to analyse the quality of the wine,then we get a more comprehensive evaluation method. A concrete analysis is as follows:In view of the question one, we use nonparametric Kendalls coefficient ofconcordance W to establish the nonparametric statistical model,and get two gro-ups of sommeliers have significant difference in the result of the evaluation ofred wine and no significant difference in the result of the evaluation of whitewine.Combined with deviation analysis,we get the second group sommeliers the result of the evaluation more reliable.In view of the question two, we use the principal component analysis to process the original data,and then use the clustering analysis methodology,to mathematical modelling.we use the SPSS software to divide the wine grape into 6level.In view of the question three,we use the MATLAB software to solve regr-ession coefficient,to build a statistical regression model,and get between wine grape and the physicochemical indexes of the wines statistical regression model.In view of the question four,we use the EXCEL software fitting to analysis variables and the MATLAB software to solve regression coefficient.And we buildbetween wine grape, the physicochemical indexes of the wine and the quality of the wines statistical regression model.On this basis, further promotion,we join aromatic substance in the influence on the quality of the wine,and setup corresponding statistical regression model.Keyword: principal component analysis;clustering analysis;statistical regression;the MATLAB software; the SPSS softwareThe problem restatementWe know each sommelier mark in the classification index after taste the wine,and then summing the total score,to determine the quality of the wine. The stand or fall of wine grape and the quality of the has direct relation. Wine and wine grape detection of the physicochemical indexes will be reflect the quality of the wine and grape in a certain extent. According to the attachment 1,2,3 the given data,to solve the following questions:1. Analysis two groups of sommeliers the results in the attachment 1 have significant difference or not, which a set of results more reliable?2. According to the physicochemical indexes of wine grape and the quality of the wine on these wine grape were classified.3. Contact analysis between wine grape and the physicochemical indexes of wine.4. Analysis of grape wine and the physicochemical indexes of wine to the influence on the quality of wine,and argument if we can use grape and the physicochemical indexes of wine to evaluate the quality of the wine?model assumption1. Assume that determine the quality of the wines factors only related to the evaluation of wine marking, grape, the physicochemical indexes of wine and aromatic substances; 2. Assume that we dont consider origin, climate, tree age, picking, production, technology and so on the influence on the quality of wine;3. Assume that wine grape reflects the characteristics of the grapes;4. Assume that sommlelier have sensitive perception highly, rich experience, accuracy estimate.Symbol descriptionSymbolMeaningtwo groups of sommeliers the results in wine-tasting have significant differencetwo groups of sommeliers the results in wine-tasting have no significant differenceSaid sommeliers numberSaid wine samples numberSaid the tatal of assigning to ith wine samples rank Said the score that the sommelier j grade to wine sample i Said the last score that the sommelier j grade to wine sample i Said and s variance Said original variable of principal component analysis Said the number of Index vectorSaid Demand variable of principal component analysis Said the number of Demand variableSaid characteristic root of matrix Said Unit feature vector of corresponding Said the ratio of Expect variation informationSaid the original data of clustering analysis Said average value of clustering analysis original dataSaid standard deviation of clustering analysis original data Said Standardized dataThe distance between sample The distance between the two typesSaid principal component rth of the physicochemical indexes of wineSaid principal component of the physicochemical indexes of white wine and red wineSaid regression coefficientdata kafangstatisticsSaid principal component of grape, the physicochemical indexes of wine or aromatic substanceSaid the quality of the wineModel analysis and establish4.1 model one4.1.1 analysis of model one 1. We try to get the summation about wines each project score from everysommeliers,and then to analysis. Removes a high scores, remove a minimum points,we work out the rest of the grades arithmetic average.We think that is the sommeliers final score. In the list below TPWine samplesRed WineWhite WineFirst setSecond setFirst setSecond setWine sample 162.6368.6383.0078.25Wine sample 280.1373.6374.6376.75Wine sample 380.7575.1378.3878.00Wine sample 468.8871.6380.1377.38Wine sample 573.3872.2572.3881.38Wine sample 672.1366.2569.3875.63Wine sample 771.5066.5078.1375.13Wine sample 871.7566.3871.8872.75Wine sample 980.8878.5073.6382.63Wine sample1074.0068.0076.2580.88Wine sample1168.8862.3873.6371.38Wine sample1253.7568.7564.2574.50Wine sample1375.3868.5066.7574.50Wine sample1473.8872.7571.5076.75Wine sample1557.6366.2574.2579.13Wine sample1675.0069.6375.0066.88Wine sample1778.5074.7580.3880.75Wine sample1860.0064.8875.1377.00Wine sample1979.1372.8872.0076.88Wine sample2079.0076.0078.6377.38Wine sample2176.8872.5078.3881.13Wine sample2276.3871.8871.5079.88Wine sample2385.3877.6375.7587.63Wine sample2477.7571.6373.8876.63Wine sample2568.8867.2577.0081.88Wine sample2674.6371.7581.7575.88Wine sample2772.7571.1366.3877.88Wine sample2882.1380.13we use nonparametric Kendalls coefficient ofconcordance W to establish the nonparametric statistical model,and judge two groups of evaluation results have significant difference or not.2. We evaluate rationality for subjective rating.The traditional evaluation method confined to scores surface numerical relationship, and ignore practical significance of the score. To evaluation which group of sommelier more credible is to analysis Which group of sommelier s scoring results more reasonable.If the score of sommelier and the final score of wine simple deviation smaller and more stable, The results more reliable. Through the deviation analysis, we got two groups score deviation.4.1.2 the establishment and solution of model one 1. Nonparametric statistical model 1) Establish inspection hypothesis 2) Calculation test statistic 3) Determine the value of P,then inference conclusion Due to the m and n Kendall W beyond consistency coefficient tables range,we can be used large sample to approximate calculation data kafang We use the SPSS software to realize, and get result According to the degree of freedom,table look-at of kafang data,we get Calculation kafang data,so,then in the level ,refuse,accept.Calculation kafang data,so,then in the level ,refuse,accept. 2. variance analysis By the formula availableDeviation of the first group sommeliers score and final score list (red wine)sommelier123456789101185 1014 1888 1837 2327 1454 580 940 1809 1288 Deviation of the second group sommeliers score and final score list (red wine)sommelier12345678910351 467 1597 1518 1882 373 614 776 396 301 Deviation of the first group sommeliers score and final score list (white wine)sommelier123456789101454124562629410589225571424148317901243Deviation of the second group sommeliers score and final score list (white wine)sommelier123456789108356405037856931101640802310701216Smaller of and samller of Deviation of sommeliers score and final score, More stable of sommeliers score and higher of level.4.2 model two4.2.1 analysis of model two 1.Due to the original data is more, we use the principal component analysis to comprehensive analysis information that the physicochemical indexes of the wine grape and the quality of the wine carry, and draw some potential comprehensive index which is the main component.2.Based on the principal component which we acquire, we use cluster anal-ysis to classify wine grape.3.According to the first principal component,we classified all kinds of winegrape.4.2.2 the establishment and solution of model two 1. principal component analysis and uncorrelated,it means is bigger,so can said variation information.That is to say new variable can represent primitive variable s most variation information and reduce the dimension. Observed the times,we get observation data matrix forAmongWe get the model is AbbreviatedDo standardized transformation for to make ,When ,we can Prove the following conclusion:1) ,we orderfrom big to small,and might as well set,to get.2) When feature vector is not equal and orthogonal to each other, it means is uncorrelated,so Is not related,either.3)Because , variation information named by and variation information named by is consistent.We can through select to determine appropriate ,just asWe value toordinary, it said choose variable named by can represent primitive variable named by s variation information in the per-cent of .We can say is principal component,and is principalcomponent named by rate of contribution to . We synthesis the physicochemical indexes of the wine grape,the physicoch-emical indexes of the wine the wine and aroma substance of the wines compo-sition matrix which we get, the list below:Red GrapesPhysical and chemical composition of red grapePhysical and chemical composition of red wineAromatic substance of red grapeAromatic substance of red wineprincipal componentsprincipal componentsprincipal componentsprincipal components12312123123Wine sample10.86315890.4222867-0.2228020.9580552-0.2504960.7950591-0.024389-0.5209430.86147510.28513930.380061Wine sample20.95445780.1120162-0.2519670.9724494-0.2144120.88215010.3650937-0.2744250.93312660.23325890.0042708Wine sample30.895343-0.356663-0.2322020.9783265-0.1948290.8332434-0.5076930.16326680.87033780.1944397-0.430953Wine sample40.9747408-0.1975830.00882840.9989698-0.0194340.870049-0.143665-0.3722210.9601822-0.2205420.0861196Wine sample50.9625192-0.0306970.25762180.9927924-0.1181450.8606111-0.4748070.1530660.9611983-0.2276880.0912833Wine sample60.9345967-0.307789-0.130250.98490050.1574130.8816809-0.3840620.19491940.9536208-0.08724-0.148974Wine sample70.9825768-0.013562-0.1179720.95323370.27800820.7761247-0.430585-0.4119130.8995842-0.2749560.1454389Wine sample80.84618480.4494233-0.2652160.9637746-0.2377010.70936010.68881490.03997550.83336630.2379032-0.441026Wine sample90.990599-0.005803-0.0803870.9770249-0.200360.81051710.5214980.0887380.9814982-0.084765-0.04426Wine sample100.95533630.27334390.06872360.99227620.08899250.93222090.00440330.21618770.9699324-0.1992320.0157761Wine sample110.9743835-0.1861410.02796380.27720070.92291580.8336915-0.4559830.08686540.64917870.72169940.0653246Wine sample120.9508522-0.262167-0.0926230.94553080.28641550.65218660.70325820.18844730.52351990.72428540.3209318Wine sample130.9672856-0.0448730.15025940.9987022-0.0469080.65845170.72961950.15189230.9657076-0.1822570.079069Wine sample140.81924980.5200173-0.1969290.9921691-0.1184210.853923-0.362235-0.0693530.58846560.70919660.3280064Wine sample150.9778608-0.0998390.05092440.98710640.13125940.80184520.58550380.01581710.94146870.10704490.170754Wine sample160.96362330.2222475-0.0828080.99909420.01729450.8789802-0.4647350.07295440.9475358-0.2605570.1116898Wine sample170.9653827-0.046220.24729040.9965336-0.0806840.67038910.72723960.00336490.9771463-0.126009-0.010365Wine sample180.9499053-0.249512-0.0974490.90653170.37995740.7854615-0.5409610.27632480.74440590.3094358-0.543335Wine sample190.9839563-0.16136-0.0427770.9989314-0.0220070.8461097-0.4872390.18221490.9673794-0.1965820.0336832Wine sample200.9527207-0.1039770.19123980.91061580.38093340.8283732-0.006798-0.3882530.85278980.2371185-0.356619Wine sample210.9145542-0.338181-0.1928520.9861032-0.1594420.62837610.76366410.10012150.9842411-0.034084-0.137905Wine sample220.97386070.0046596-0.0669180.9948828-0.0965970.8072754-0.533350.24650610.9047465-0.179232-0.027421Wine sample230.9952603-0.023362-0.0554750.9755845-0.2055790.807886-0.026028-0.4805190.9627063-0.1921820.0345788Wine sample240.9550987-0.0185020.26287160.994175-0.1021810.9526554-0.222989-0.0865850.97104390.16752670.0091426Wine sample250.96048180.02960870.24862620.99600680.03940710.81490230.56376850.03269370.9159145-0.2400220.1797315Wine sample260.67408720.20306710.67917960.9930860.0122170.82932990.51525870.20519830.956983-0.2408870.0949602Wine sample270.89319730.43290780.03334430.99435240.09886560.9213727-0.2675830.22309110.8396128-0.2226740.098578White GrapePhysical and chemical composition of white grapePhysical and chemical composition of white wineAromatic substance of white grapeAromatic substance of white wineprincipal componentsprincipalcomponentscomponentsAromatic substance12112123Wine sample10.64981090.21751740.99961560.82668770.55123770.9574016-0.2585580.0302364Wine sample20.9881586-0.1071720.99984050.7934462-0.53680.9814656-0.1484280.0084029Wine sample30.9243747-0.3007990.9979260.8388836-0.4951140.9151993-0.370957-0.127534Wine sample40.9759775-0.1654920.99990430.9883211-0.1111070.9814701-0.155704-0.017768Wine sample50.9659196-0.1941150.99971660.9718889-0.2039930.80849440.5350403-0.188868Wine sample60.9935091-0.0405670.99972390.7543130.64888750.9452541-0.1428390.1956097Wine sample70.9916997-0.0687620.99894940.82688450.54798210.50850180.64724640.149058Wine sample80.97391110.13774730.99969540.73199730.67384450.9370474-0.2333120.0700476Wine sample90.9856982-0.1502750.9996880.9096509-0.3700680.9806276-0.056408-0.007606Wine sample100.9919644-0.0482360.99984310.8519535-0.4626180.9811777-0.1232870.0241336Wine sample110.98906350.06059860.99982460.94192330.31725610.72253470.6514228-0.15915Wine sample120.9851764-0.0123140.99990840.57213650.81446720.77818250.5715173-0.208869Wine sample130.85849270.29110550.99964680.9193928-0.1240820.93255350.0593490.2297124Wine sample140.94936610.25496840.99964160.604480.79245780.95830850.19473060.0985627Wine sample150.9569135-0.1555450.99926210.86050240.41503930.80594040.5360153-0.067456Wine sample160.95937660.15899940.99991160.9375783-0.2813890.4490976-0.0109140.8609446Wine sample170.98702460.0406830.99984220.938942-0.2906650.9286195-0.32498-0.024001Wine sample180.97722180.06536980.9993960.8272473-0.4090490.8787402-0.037976-0.097333Wine sample190.92789680.06642950.99665170.60807280.77939040.9398176-0.022325-0.194653Wine sample200.9923051-0.1026960.99973490.9434813-0.2878470.9735623-0.1833330.0108662Wine sample210.76971580.5160660.99925260.8964785-0.4254210.9456885-0.154384-0.078705Wine sample220.9839697-0.1104260.99948680.96687490.01397510.9633665-0.1569640.0390649Wine sample230.86737720.40221420.99938220.90388630.4136610.9529853-0.211088-0.115592Wine sample240.9690346-0.1461180.99880510.8432693-0.44130.9570619-0.253613-0.077788Wine sample250.9897617-0.0974950.99972260.8230772-0.4997930.9649392-0.2435810.0245133Wine sample260.966363-0.2094930.99839790.990095-0.0887870.6196290.64954160.3125943Wine sample270.932310.22638650.99528830.6360523-0.322060.9478117-0.22752-0.104717Wine sample280.946265-0.2676490.98801530.96214520.25428260.85997990.4572425-0.0846962. Cluster analysis 1) Using the standard deviation to standardized process original data 2) Using Euclidean distance formula to get the distance between sample3) Using the mean distance class distance, namely two classes of the two elements between the mean distance as two kinds of distanceNamely model for3.Using the SPSS software to calculate, we get Classification treeThe classification diagram of red grape treeThe classification of white grape tree diagram4.2.3 the solution of model twoCombined with the physicochemical indexes of the red grapes the first principal component and cluster