店铺租金的确定模型

1、店铺租金的确定模型某商人欲在某火车站附近经营一店铺，委托本小组对相关情况进行调查。经过数月的资料收集和整理，我们的调查成果如下：进出车站的乘客为主要服务对象的10家便利店的数据。y是日均销售额，x1为店铺面积，x2是店铺距车站的距离，x3为店员人数，x4为店铺日租金。具体数据如下表：店铺代码日均销售额（元）y店铺面积（m2）x1离车站距离（100m）x2店员人数（人）x3店铺日租金（元）x4abcdefghij400045008000600050002000150090003000700060100855075557095456535213461325753545644600600102075

2、07504402801425450780数据来源：为了考察店铺面积、离车站距离、店员人数和日租金对日销售额的影响，我们首先做y关于x1、x2、x3、x4的回归，即建立如下回归模型：y=c+1 x1+2 x2+3 x3+4 x4得回归结果如下表：dependent variable: ymethod: least squaresdate: 12/14/03 time: 17:51sample: 1 10included observations: 10variablecoefficientstd. errort-statisticprob. c4815.267153

3、6.4183.1340870.0258x1128.193039.797963.2210960.0234x2-1494.966513.4078-2.9118480.0333x3-619.1674472.6664-1.3099460.2472x4-1.8772082.938471-0.6388380.5510r-squared0.970270 mean dependent var5000.000adjusted r-squared0.946486 s.d. dependent var2505.549s.e. of regression579.6124 akaike info criterion15

4、.86945sum squared resid1679752. schwarz criterion16.02074log likelihood-74.34724 f-statistic40.79489durbin-watson stat1.407218 prob(f-statistic)0.000522从回归结果来看， r2接近于1，整个方程的拟合优度很高，ff0.05(4，5)5.19，变量x3、x4对应的偏回归系数之t值小于2，而且x3、x4的符号与经济意义相悖，该模型明显存在多重共线性，回归结果不显著，回归方程不能投入使用。由于变量较多，采用逐步回归法来修正模型。用y对各个变量单独进行回

5、归：对x1，有：dependent variable: ymethod: least squaresdate: 12/14/03 time: 20:17sample: 1 10included observations: 10variablecoefficientstd. errort-statisticprob. c444.44442988.5550.1487160.8855x165.0793741.384151.5725670.1545r-squared0.236129 mean dependent var5000.000adjusted r-squared0.140645 s.d. de

6、pendent var2505.549s.e. of regression2322.680 akaike info criterion18.51569sum squared resid43158730 schwarz criterion18.57620log likelihood-90.57844 f-statistic2.472968durbin-watson stat1.988381 prob(f-statistic)0.154464对x2，有：dependent variable: ymethod: least squaresdate: 12/14/03 time: 20:20sampl

7、e: 1 10included observations: 10variablecoefficientstd. errort-statisticprob. c8687.5001096.2327.9248710.0000x2-1229.167324.6760-3.7858260.0053r-squared0.641777 mean dependent var5000.000adjusted r-squared0.596999 s.d. dependent var2505.549s.e. of regression1590.581 akaike info criterion17.75844sum

8、squared resid20239583 schwarz criterion17.81896log likelihood-86.79221 f-statistic14.33248durbin-watson stat2.488527 prob(f-statistic)0.005344对x3，有：dependent variable: ymethod: least squaresdate: 12/14/03 time: 20:28sample: 1 10included observations: 10variablecoefficientstd. errort-statisticprob. c

9、3344.8283791.3250.8822320.4034x3344.8276770.69640.4474230.6664r-squared0.024413 mean dependent var5000.000adjusted r-squared-0.097536 s.d. dependent var2505.549s.e. of regression2624.897 akaike info criterion18.76033sum squared resid55120690 schwarz criterion18.82084log likelihood-91.80164 f-statist

10、ic0.200188durbin-watson stat2.273575 prob(f-statistic)0.666436对x4，有：dependent variable: ymethod: least squaresdate: 12/14/03 time: 20:30sample: 1 10included observations: 10variablecoefficientstd. errort-statisticprob. c-124.4556691.7552-0.1799130.8617x47.2226300.8931328.0868540.0000r-squared0.89100

11、4 mean dependent var5000.000adjusted r-squared0.877380 s.d. dependent var2505.549s.e. of regression877.3734 akaike info criterion16.56860sum squared resid6158272. schwarz criterion16.62912log likelihood-80.84299 f-statistic65.39721durbin-watson stat1.099477 prob(f-statistic)0.000040从上面的回归结果可以看到，y对x2

12、的回归拟合最好，故选择该回归式为基本回归表达式。现在分别加入x1、x3、x4回归结果如下：加入x1，有：dependent variable: ymethod: least squaresdate: 12/14/03 time: 21:21sample: 1 10included observations: 10variablecoefficientstd. errort-statisticprob. c3641.214817.19384.4557530.0030x175.4584910.588697.1263260.0002x2-1307.769121.3087-10.780500.0000

13、r-squared0.956605 mean dependent var5000.000adjusted r-squared0.944206 s.d. dependent var2505.549s.e. of regression591.8273 akaike info criterion15.84763sum squared resid2451817. schwarz criterion15.93841log likelihood-76.23816 f-statistic77.15446durbin-watson stat1.809788 prob(f-statistic)0.000017可

14、见，加入x1效果较好，这样回归式中就有x1、x2两个变量了。在此基础上继续加入其他变量。加入x3，有：dependent variable: ymethod: least squaresdate: 12/14/03 time: 21:26sample: 1 10included observations: 10variablecoefficientstd. errort-statisticprob. c3993.580797.84105.0054840.0024x1109.374725.406914.3049200.0051x2-1181.338142.6370-8.2821300.0002x

15、3-647.0407446.8316-1.4480640.1978r-squared0.967843 mean dependent var5000.000adjusted r-squared0.951765 s.d. dependent var2505.549s.e. of regression550.2815 akaike info criterion15.74791sum squared resid1816859. schwarz criterion15.86895log likelihood-74.73956 f-statistic60.19526durbin-watson stat1.

16、281362 prob(f-statistic)0.000072可以看出，加入了x3以后引起了多重共线性，故剔除。现在加入x4，回归结果如下：dependent variable: ymethod: least squaresdate: 12/14/03 time: 21:29sample: 1 10included observations: 10variablecoefficientstd. errort-statisticprob. c4636.4821619.0772.8636580.0287x199.5763235.195072.8292690.0300x2-1674.283523.

17、5131-3.1981670.0186x4-2.2325263.095576-0.7211990.4979r-squared0.960067 mean dependent var5000.000adjusted r-squared0.940100 s.d. dependent var2505.549s.e. of regression613.2195 akaike info criterion15.96450sum squared resid2256229. schwarz criterion16.08553log likelihood-75.82249 f-statistic48.08356durbin-watson stat1.907328 prob(f-