计量经济学论文-驾校服务对顾客满意度的调查研究

1、.驾校服务对顾客满意度的调查研究指导老师 卢君生 论文成员 姜强 2013192121 王晶 2013192152 周凯文 2013192174 刘琴 2013192132 昌桑 2013192103 一 引言 在市场竞争中，价格作为供需关系的纽带，有着极其重要的作用。从全局看，需求理论广为人们所接受，即价格降低，则市场需求量增大，反之，若价格上升，则市场需求量便会减少。然而作为一个独立的企业角度来看，在一定范围内，价格并不是其获取利润的唯一策略，甚至不是主要策略。通常企业为获取利润会采取多种策略，而这些策略都是以围绕价格和服务为核心对不同的对象和顾客制定的，本文将以襄阳市襄城区部分驾校市场进

2、行调查与研究。二 文献综述该论文以实地调研为基础，以襄阳市襄城区部分驾校市场（C类车驾校市场）为例进行研究和分析，其目的在于研究驾校服务对其顾客满意度的影响分析。随着私家车市场的兴起，伴随着日益增大的还有驾校市场，越来越多的驾校为了盈利，过多的依赖价格策略，以降价为核心的价格策略吸引学员入学，然而降价实际上缩小了驾校的利润空间，而且大多时候顾客对其微小的降价并不敏感，因此本文以计量经济学的研究方式研究驾校服务与顾客满意度之间的关系，反映顾客对服务的敏感性。本文采用了文献研究法，eviews分析法等方法，针对驾校服务对驾校满意度进行了分析评价，得出了驾校服务对驾校满意度的影响程度。该体系主要包括

3、驾校服务性，驾校的时效性，便利性，驾校的收费情况。通过参考本文的研究，各驾校可以看到每种服务对驾校满意度的影响程度，从而结合自身的情况进行改进，使驾校更具有竞争性。目前有关此方面的研究文献还较少，故本文研究内容具有一定的挑战性和新颖性。关键词：驾校服务性 驾校便利性 驾校实效性 驾校满意度 eviews分析法三 理论模型与数据 为详细调查驾校服务对其顾客满意度（Y）的影响，论文将驾校服务分为了三个方面，分别为服务性(X1)，时效性(X2)和便利性(X3)，此外还增设了一个独立变量，即价格（X4），其中各变量含义及内容均在论文附件中表明。因此，初步设立假设模型为：Y=0+1X1+2X2+3X3+

4、4X4+为估计参数模型，实地随机调研了23组真实数据，数据如下：Y(满意度)X1（服务性） X2（时效性）X3（便利性）X4(价格/元）5548735005941293800634141132006461693000651416113200711620930507114161132007318181329507312229295073182411295075232415295079192213300080212411290082192613290091212813290092212823290093232617286010019281529001022328192800103252619260

5、0104272821270010727301926001142732232500四 建模过程1.建立多元回归模型Dependent Variable: YMethod: Least SquaresDate: 06/24/15 Time: 15:31Sample: 1 23Included observations: 23VariableCoefficientStd. Errort-StatisticProb. C56.6428234.261491.6532510.1156X10.0381460.4148360.0919540.9277X21.2209730.5026572.4290400.02

6、58X31.4943550.4352633.4332230.0030X4-0.0078900.009001-0.8765560.3923R-squared0.912411 Mean dependent var82.13043Adjusted R-squared0.892946 S.D. dependent var17.02753S.E. of regression5.571249 Akaike info criterion6.462776Sum squared resid558.6986 Schwarz criterion6.709622Log likelihood-69.32192 F-st

7、atistic46.87607Durbin-Watson stat1.343999 Prob(F-statistic)0.000000由软件运行结果得到初步参数模型为： Y=0.038X1+1.22X2+1.494X3-0.0079X4+56.64观测模型可知，t检验值普遍偏小，P-value值较大，拟合优度虽然较高，但模型并不完美，须进一步检验。2.多重共线性检验1）.相关系数检验由表可知，因变量之间线性相关程度较高，可认为该模型具有多重共线性。2）. 辅助回归方程检验Dependent Variable: X1Method: Least SquaresDate: 06/17/15 Time

8、: 11:09Sample: 1 23Included observations: 23VariableCoefficientStd. Errort-StatisticProb. C17.9107318.496640.9683230.3450X20.6231700.2384022.6139470.0171X30.3085710.2300681.3412190.1957X4-0.0062590.004766-1.3132490.2047R-squared0.849379 Mean dependent var17.60870Adjusted R-squared0.825597 S.D. depen

9、dent var7.377725S.E. of regression3.081055 Akaike info criterion5.245192Sum squared resid180.3651 Schwarz criterion5.442669Log likelihood-56.31971 F-statistic35.71487Durbin-Watson stat1.581363 Prob(F-statistic)0.000000Dependent Variable: X2Method: Least SquaresDate: 06/17/15 Time: 11:10Sample: 1 23I

10、ncluded observations: 23VariableCoefficientStd. Errort-StatisticProb. C34.8623413.437012.5945000.0178X10.4244400.1623752.6139470.0171X30.1998800.1932921.0340810.3141X4-0.0076400.003715-2.0563720.0537R-squared0.861605 Mean dependent var22.43478Adjusted R-squared0.839754 S.D. dependent var6.351994S.E.

11、 of regression2.542754 Akaike info criterion4.861143Sum squared resid122.8464 Schwarz criterion5.058621Log likelihood-51.90315 F-statistic39.42950Durbin-Watson stat1.612515 Prob(F-statistic)0.000000Dependent Variable: X3Method: Least SquaresDate: 06/17/15 Time: 11:10Sample: 1 23Included observations

12、: 23VariableCoefficientStd. Errort-StatisticProb. C5.49941118.014210.3052820.7635X10.2802880.2089801.3412190.1957X20.2665680.2577821.0340810.3141X4-0.0008280.004740-0.1746470.8632R-squared0.668995 Mean dependent var13.95652Adjusted R-squared0.616731 S.D. dependent var4.743208S.E. of regression2.9364

13、60 Akaike info criterion5.149057Sum squared resid163.8331 Schwarz criterion5.346535Log likelihood-55.21416 F-statistic12.80031Durbin-Watson stat1.468753 Prob(F-statistic)0.000083Dependent Variable: X4Method: Least SquaresDate: 06/17/15 Time: 11:11Sample: 1 23Included observations: 23VariableCoeffici

14、entStd. Errort-StatisticProb. C3765.703127.524729.529200.0000X1-13.2953710.12403-1.3132490.2047X2-23.8276711.58724-2.0563720.0537X3-1.93601511.08529-0.1746470.8632R-squared0.783713 Mean dependent var2970.000Adjusted R-squared0.749563 S.D. dependent var283.7573S.E. of regression142.0026 Akaike info c

15、riterion12.90634Sum squared resid383130.1 Schwarz criterion13.10382Log likelihood-144.4229 F-statistic22.94880Durbin-Watson stat1.887221 Prob(F-statistic)0.0000023）.建立基本的一元回归方程根据相关系数分析，顾客满意度与时效性因素关联程度最大，因此，建立一元回归模型为： Y=+X2+4）.逐步引入其它变量模型x1x2x3x4R-squaredAdjusted R-squaredY=f(X2)2.4544(10,4359) 0.8383

16、0.83065Y=f(X1,x2)0.5834(1.2489)1.8418(3.3941)0.850.835Y=f(X2,x3)1.5295(5.1128)1.5596(3.8932)0.90840.8988Y=f(X2,x4)1.9722(4.1458)-0.0124(-1.1631)0.84860.8334Y=f(X1,x2,x3)0.143(0.3623)1.4089(3.1184)1.5096(3.4925)0.90860.8942Y=f(X2,x3,x4)1.2447(2.9659)1.5061(3.7187)-0.0081(-0.9688)0.91230.8985Y=f(X1,x2

17、,x3,x4)0.0381(0.0919)1.2209(2.429)1.4944(3.4332)-0.0079(-0.8765)0.91240.8929当已经引入X2和X3后，模型已经比较完美，当继续引入X3或X4时，虽然拟合优度在增大，但却破坏了t检验值，因此X3、X4予以剔除。Dependent Variable: YMethod: Least SquaresDate: 06/24/15 Time: 16:07Sample: 1 23Included observations: 23VariableCoefficientStd. Errort-StatisticProb. C26.0497

18、04.2394876.1445410.0000X21.5294660.2991465.1127750.0001X31.5596650.4006093.8932330.0009R-squared0.908041 Mean dependent var82.13043Adjusted R-squared0.898845 S.D. dependent var17.02753S.E. of regression5.415592 Akaike info criterion6.337549Sum squared resid586.5728 Schwarz criterion6.485657Log likel

19、ihood-69.88182 F-statistic98.74369Durbin-Watson stat1.373572 Prob(F-statistic)0.000000即改善模型为：Y=1.5295X1+1.5597X2+26.0497t=(5.1128) (3.8932) (6.1445) R-squared=0.908Adjusted R-squared=0.8988 F=98.74363.异方差性检验1）.图形分析 根据上面二图，可观察出随着便利性和时效性的提高，其离散程度有些许扩大，但不易观察出是否有离散性。2）. Goldfeld-Quant 检验1 按样本解释变量排序并分成两部

20、分（分别有1 到10 共10 个样本和14 到23 共10 个样本）2 用样本1建立回归模型1，其残差平方和为43.0215SMPL 1 10 LS Y C X2 X3Dependent Variable: YMethod: Least SquaresDate: 06/24/15 Time: 16:50Sample: 1 10Included observations: 10VariableCoefficientStd. Errort-StatisticProb. C38.459545.0202147.6609370.0001X21.0961400.1925405.6930380.0007X3

21、1.0044540.5345451.8790840.1023R-squared0.885611 Mean dependent var66.70000Adjusted R-squared0.852929 S.D. dependent var6.464433S.E. of regression2.479099 Akaike info criterion4.896993Sum squared resid43.02153 Schwarz criterion4.987768Log likelihood-21.48496 F-statistic27.09747Durbin-Watson stat1.135

22、787 Prob(F-statistic)0.0005063 用样本2 建立回归模型2，其残差平方和为291.9732SMPL 14 23 LS Y C X2 X3Dependent Variable: YMethod: Least SquaresDate: 06/24/15 Time: 16:53Sample: 14 23Included observations: 10VariableCoefficientStd. Errort-StatisticProb. C8.28125032.722860.2530720.8075X22.6205361.3300261.9702880.0895X30

23、.9419640.6822891.3805940.2099R-squared0.624520 Mean dependent var98.80000Adjusted R-squared0.517240 S.D. dependent var9.295160S.E. of regression6.458364 Akaike info criterion6.811954Sum squared resid291.9732 Schwarz criterion6.902730Log likelihood-31.05977 F-statistic5.821403Durbin-Watson stat1.1079

24、29 Prob(F-statistic)0.032438(4)算F 统计量：F = RSS2/ RSS1 291.9732/43.0215=6.788， RSS1和RSS2 分别是模型1和模型2的残差平方和。取= 0.05时，查F分布表得F0。05 (101 10,1 1 1)= 3.44 ，而F = 6.788>F0。05= 3.44 ，所以存在异方差性。3).调整异方差性（GLS）（1）求出OLS回归模型Dependent Variable: YMethod: Least SquaresDate: 06/25/15 Time: 10:43Sample: 1 23Included ob

25、servations: 23VariableCoefficientStd. Errort-StatisticProb. C26.049704.2394876.1445410.0000X21.5294660.2991465.1127750.0001X31.5596650.4006093.8932330.0009R-squared0.908041 Mean dependent var82.13043Adjusted R-squared0.898845 S.D. dependent var17.02753S.E. of regression5.415592 Akaike info criterion

26、6.337549Sum squared resid586.5728 Schwarz criterion6.485657Log likelihood-69.88182 F-statistic98.74369Durbin-Watson stat1.373572 Prob(F-statistic)0.000000（2）生成误差序列（3）建立GLS模型Dependent Variable: YMethod: Least SquaresDate: 06/25/15 Time: 10:53Sample: 1 23Included observations: 23Weighting series: 1/AB

27、S(RE1)VariableCoefficientStd. Errort-StatisticProb. C25.351430.90051628.152120.0000X21.5318430.04130937.082900.0000X31.6264250.08583918.947430.0000Weighted StatisticsR-squared0.999973 Mean dependent var77.33649Adjusted R-squared0.999971 S.D. dependent var158.9987S.E. of regression0.861179 Akaike info criterion2.660079Sum squared resid14.83258 Schwarz criterion2.808187Log likelihood-27.59