基于多元线性回归分析餐饮业营业收入的影响因素Word下载.docx

1、2115天津市103.614442.01423575777612.31472河北省45.228442.954674000723357.27333山西省7512665.255686759913339.43630内蒙古自治区65.516916.5427361287455.22498辽宁省142.727213.227154652819659.54390吉林省20.213046.4162110167745.32751黑龙江省24.614454.912101592410058.63835上海市469.521818.15163821675520486.32415江苏省399.559753.37240222

2、853133823.97939浙江省26137756.591506136523289235498安徽省100.119229.3410327284812924.96030福建省158.921868.497977612311847.33774江西省31.814410.19241257789725.24522山东省335.955230.32243015680829796.19733河南省150.432191.313747647720232.19413湖北省225.824791.831861126525155075799湖南省109.324621.675875859114539.76691广东省604

3、.162474.79288333722449891.310644广西壮族自治区3214449.9303304569118.94719海南省10.43177.568485532465.4895重庆市16312783.261015802979622.32970四川省231.326392.07161114034822597.38107贵州省21.18086.86304196455919.13502云南省49.611832.31360311898969.84687西藏自治区0.9815.6713734496312陕西省125.616205.459589120212249.43764甘肃省30.7633

4、0.69346243015878.52582青海省3.72122.063735371504.2578宁夏回族自治区8.62577.579080811887.2654新疆维吾尔自治区12.28443.847788705884.52264数据标准化处理后：2.29149-0.042141.201551.874750.79709-0.81002-0.26081-0.38325-0.5507-0.26829-0.62394-1.04084-0.618170.50798-0.49559-0.48230.821961.06311-0.43582-0.49635-0.36908-0.14621-0.098-

5、0.26617-0.49395-0.22574-0.54569-0.52954-0.63836-0.67253-0.021560.4297-0.18497-0.402870.482390.00665-0.77115-0.47209-0.8776-0.83542-0.61172-0.58171-0.74423-0.38243-0.81748-0.77564-0.39929-0.192581.978180.086280.971091.670570.55832-0.702331.549842.501041.9281.8141.783151.280650.702341.100840.805760.69

6、3311.333090.40439-0.28223-0.078510.21208-0.08228-0.136060.595370.077580.08948-0.08226-0.04239-0.23502-0.21448-0.70017-0.38527-0.77865-0.65561-0.42990.054031.160672.213121.963070.940391.413271.924650.025560.746580.64043-0.038080.534981.809780.486950.275571.25040.571530.101060.51244-0.225940.26474-0.3

7、4529-0.255930.012230.832652.801822.674272.530453.137933.258662.25168-0.69895-0.38275-0.701-0.59863-0.485580.12475-0.83112-1.10029-0.97529-0.86542-1.09659-1.247970.10266-0.488840.190780.00845-0.43935-0.50310.52060.377430.937270.73990.752181.34096-0.76564-0.78778-0.69974-0.73031-0.77943-0.31212-0.5912

8、5-0.54937-0.6296-0.5897-0.499270.11326-0.88925-1.25063-1.06422-0.96066-1.27745-1.45725-0.12619-0.2710.119390.14128-0.1981-0.21807-0.7069-0.89957-0.64714-0.6736-0.78316-0.64238-0.87212-1.16747-1.03416-0.92651-1.18486-1.36177-0.84213-1.13848-0.96778-0.87117-1.14969-1.33448-0.8201-0.76506-0.98406-0.861

9、56-0.7826-0.75653三、参数估计利用Eviews6.0估计模型参数，最小二乘法的回归结果如下：用Eviews估计结果为：根据表中的样本数据，模型估计结果为：t= （3.33E-05） （-0.111061） （1.617020） （4.861177） （1.004762） （-2.675348）可以看出，可决系数，修正的可决系数 。说明模型的拟合程度还可以。但是当时，X1、X2、X4系数均不能通过检验，且X1、X5的系数为负，与经济意义不符，表明模型很可能存在严重的多重共线性四、模型检验1.多重共线性的检验：计算各个解释变量的相关系数，得到相关系数矩阵表4.1 相关系数矩阵由相关

10、系数矩阵可以看出，解释变量X1、X2、X4之间存在较高的相关系数，证实确实存在严重的多重共线性。2多重共线性修正表4.2 一元回归结果变量X1X2X3X4X5参数估计值0.7524430.9192390.9837480.8466990.486108t 统计值6.15198012.5737529.506038.5695562.9955100.5661730.8450020.9677640.7169000.2363010.5512130.8396570.9666520.7071380.209967其中，X3的方程最大，以X3为基础，顺次加入其它变量逐步回归。表4.3加入新变量的回归结果（一）X3，X1-0.054372（-1.0085