Econometrics reportClass number:No number:Eglish name:Chinese name:ContentsBackground andData Analysis 2-5 and modelT-test 6-8F-test 8-10Summary,and,suggestion 11BACKGROUND●The report below is about the food sales , I instance theresident population (10 000 ) , per capita income thefirst year , meat sales , egg sales , the fish sales .●In order to build mathematical models to understand therelationship of each variable and its food sales , and Itake statistics of Tianjin from 1994 to 2007 the demandfor foodAmongY X1 X2 X3 X4 X51 98.4500 153.2000 560.2000 6.5300 1.2300 1.89002 100.7000 190.0000 603.1100 9.1200 1.3000 2.03003 102.8000 240.3000 668.0500 8.1000 1.8000 2.71004 133.9500 301.1200 715.4700 10.1000 2.0900 3.00005 140.1300 361.0000 724.2700 10.9300 2.3900 3.29006 143.1100 420.0000 736.1300 11.8500 3.9000 5.24007 146.1500 491.7760 748.9100 12.2800 5.1300 6.83008 144.6000 501.0000 760.3200 13.5000 5.4700 8.36009 146.9400 529.2000 774.9200 15.2900 6.0900 10.070010 158.5500 552.7200 785.3000 18.1000 7.9700 12.570011 169.6800 771.7600 795.5000 19.6100 10.1800 15.120012 162.1400 811.8000 804.8000 17.2200 11.7900 18.250013 170.0900 988.4300 814.9400 18.6000 11.5400 20.590014 178.6900 1094.6500 828.7300 23.5300 11.6800 23.3700Based on the above data , the conclusions as followsThey are β value, stand error R2 freedom SST SSR-4.68859277 3.6364556 2.66771805 0.118961 0.077743 -0.16534 2.231226292 2.472067 1.26879898 0.059624 0.03818 30.26735 0.969804859 5.7740803 #N/A #N/A #N/A #N/A 51.38865853 8 #N/A #N/A #N/A #N/A 8566.490175 266.72002 #N/A #N/A #N/A #N/AWhere T statistics is-2.10135242 1.4710182 2.10255375 1.995178 2.036186 -0.00546The modelY=β0+β1X1+β2X2+β3X3+β4X4+β5X5+uY=-0.1653+0.0777X1+0.1190X2+2.6677X3+3.6365X4-4.6886X5+u （0,03818）（0.0596）（1.2688）（2.4721）（2.2312）N=14 R2=0.9698Y represents the model of food sales ( tons / year)，X1 said the resident population (10 000 ) , The X2 per capita income the first year , X3：meat sales , X4：said egg sales , X5：said the fish sales .0.0777 means when resident population increase 1 point, the other factors remain unchanged, the food sales increase 0.777 point .0.1190 means when resident population increase 1 point, the other factors remain unchanged, the food sales increase 0.1190 point .2.6677 means when resident population increase 1 point, the other factors remain unchanged, the food sales increase2.6677 point .3.6365 means when resident population increase 1 point, the other factors remain unchanged, the food sales increase 3.6365 point .-4.6886 means when resident population increase 1 point, the other factors remain unchanged, the food sales decrease 4.6886 point .t-testFor example, for a 5% level test and with n-k-1=8 degrees of freedom, the critical value is c=1.860●Null hypothesis H0: β1=0 alternative hypothesis H1: β1>0 We have 8 degrees of freedom, we can use the standard normal critical values. The 5% critical value is 1.860. tβ1(hat)= 2.036186>C we reject H0. the t statistic for β1(hat) is statistically significant at the 5% level .●Null hypothesis H0: β2=0 alternative hypothesis H2: β2>0 We have 8 degrees of freedom, we can use the standard normal critical values. The 5% critical value is 1.860. tβ2(hat)= 1.995178>C we reject H0. the t statistic for β2(hat) is statistically significant at the 5% level .●Null hypothesis H0: β3=0 alternative hypothesis H3: β3>0 We have 8 degrees of freedom, we can use the standard normal critical values. The 5% critical value is 1.860. tβ3(hat)= 2.10255375>C we reject H0. the t statistic for β3(hat) is statistically significant at the 5% level .●Null hypothesis H0: β4=0 alternative hypothesis H4: β4>0 We have 8 degrees of freedom, we can use the standard normal critical values. The 5% critical value is 1.860.tβ4(hat)= 1.4710182<C we not reject H0. the t statistic for β4(hat) is statistically insignificant at the 5% level .Null hypothesis H0: β5=0 alternative hypothesis H5: β5<0 We have 8 degrees of freedom, we can use the standard normal critical values. The 5% critical value is 1.860. tβ5(hat)= -2.10135242<-C we reject H0. the t statistic for β5(hat) is statistically significant at the 5% level .F STATISTICBecause only X1 X2 X3 X5 statistically significant. so we imposed 1 exclusion restrictions in this model.Y X1 X2 X3 X41 98.45 153.2 560.2 6.53 1.892 100.7 190 603.11 9.12 2.033 102.8 240.3 668.05 8.1 2.714 133.95 301.12 715.47 10.1 35 140.13 361 724.27 10.93 3.296 143.11 420 736.13 11.85 5.247 146.15 491.776 748.91 12.28 6.838 144.6 501 760.32 13.5 8.369 146.94 529.2 774.92 15.29 10.0710 158.55 552.72 785.3 18.1 12.5711 169.68 771.76 795.5 19.61 15.1212 162.14 811.8 804.8 17.22 18.2513 170.09 988.43 814.94 18.6 20.5914 178.69 1094.65 828.73 23.53 23.37F =( 1- R2 ur )/(n-k-1)In this form ,the model change:β0= -21.6764 β1= 0.058715 β2= 0.164331 β3= 2.353292 β4=-2.1736y=-21.6764 +0.058715X1 +0.164331X2 +2.353292X3 -2.1736 X4(0.038175) (0.054226) (1.329076) (1.523517) WhereH0: β1=0 β2=0 β3=0F =( 1- R2 ur )/(n-k-1)F=( 0.961637-6.136089)*8/(1-6.136089)/1=8.059754416Through TABLI G.3 c=2.84Since this is well below the 5% critical value, we to reject H0.the variables are jointly significant. In other words the resident population per capita income the first year meat sales the fish sales are jointly significant in the food sales.SummaryIn above data, the meat sales and the resident population is much impact in the food sales, the fish sales is less impact in food sales, Even Negative impact on the food slaes.In addition to the above can affect food sales factors , including weather, food production . if the weather is good , the food sales of course will good , in opposite ,the bad weather , the food sales will poor . and if there are much food production , will much impact on the food sales ,in opposite , less impact.。