代写英文：家庭娱乐支出的因素和变量

Y = +我团队= 116 +

H0: i=0 i=1 to 16H1: i0 i=1 to 16

H0: =1=2==16=0H1:至少有一个系数与0不同

These variables include those corresponding to the locality and geography of the respondents. The families belonging to rural areas are taken in the variable “rural” which takes a value 1 for rural respondents. 13.7% respondents are from rural areas. There are nine dummy variables corresponding to nine provinces of Canada. For k categories in a group, k-1 dummy variables are needed to avoid linear dependency (Gujarati & Sangeetha, 2003). Since there are 10 provinces for which data is collected, 9 dummies are taken for the model with the base province being Ontario. 4.3% respondents come from Newfoundland, 4.2% belong to Prince Edward Island, 7.9% come from Nova Scotia, 6.3% hail from New Brunswick, 14.9% respondents are from Quebec, 6% are from Manitoba, 8.6% are from Saskatchewan, 7.8% are from Alberta, 13.8% are from British Columbia and 26.3% belong to Ontario.
Quantitative Analysis:
The effective model estimated here is:
Y=+i=116iXi+
Where α is the constant term of the regression, βis are the coefficients estimated corresponding to different variables Xis and µ is the error term. Table 3 shows the regression results of model where the dependent variable is the log of recreational expenses.
Hypothesis testing for statistical significance of coefficients:
The hypotheses behind the t-test for statistical significance of the coefficients are:
H0: i=0 i=1 to 16H1: i0 i=1 to 16
For each coefficient, the t-statistic is calculated and compared with the critical t value from student’s t-distribution table. An alternative approach is to check the corresponding p-value of the t-statistic. If it is less than 0.1 but greater than 0.05, the coefficient is statistically different from 0 at 10% level of significance, if p-value lies between 0.01 and 0.05, the null hypothesis is rejected at 5% level of significance and if the p-value is less than 0.01, the null hypothesis is rejected at 1% level of significance. On the contrary, the null hypothesis is not rejected and the coefficient is not statistically different from zero, if the p-value is greater than 0.1.
For example, the p-value of the coefficient of lnincome is ~0 which means that the coefficient is statistically significant or the coefficient is statistically different from 0 or the null hypothesis is rejected, at 1% level of significance.
To check if the overall model is statistically significant, ANOVA or Analysis of Variance is used. Its result is shown in table 3. The null hypothesis for the test is:
H0: =1=2==16=0H1:At least one of the coefficients is different from 0
The test is done by F-test. In the given case, the F-statistic is 404.235 and its degrees of freedom are (16,10186). The corresponding p-value is ~0. Hence the null hypothesis is rejected and the model is statistically significant at 1% level of significance.