assignment代写：变量描述性统计

assignment代写：变量描述性统计

表2显示了所有变量的描述性统计。为进行分析，总共进行了10203次观测。该数据包含45.9%的男性。家庭平均受教育年限为12.851年。家庭平均收入为37094.805加元。为了确保可伸缩性并使数据更流畅，使用收入日志而不是收入日志。收入对数的平均值为10.296，标准差为0.984。4.3%的受访者来自纽芬兰,4.2%属于爱德华王子岛,7.9%来自新斯科舍省,6.3%来自新布伦瑞克,14.9%受访者来自魁北克,6%来自马尼托巴省,8.6%来自萨斯喀彻温省,7.8%来自阿尔伯塔省,13.8%来自不列颠哥伦比亚省和26.3%属于安大略。12%的家庭接受社会援助,13.7%受访者来自农村地区,61.7%的受访者都结婚了。受访者的平均年龄为48.722岁。

assignment代写：变量描述性统计
图1显示了用于娱乐开支的金额分布。为确保因变量的平滑性和离群值的减少，我们使用了娱乐支出日志，而不是直接使用该变量。康乐开支日志的平均数为7.996，标准差为1.502。很明显，数据是平滑的，没有异常值。表3为模型回归结果，因变量为游憩费用日志。模型的解释力为38.8%，在1%的显著性水平上显著。模型中存在多重共线性的可能性较小，因为存在多重共线性时，变量失去了统计意义，模型的解释力非常强(Greene, 2000)。然而，这并不适用于这个模型。由于规格误差，模型可以是异方差的。异方差的存在往往是由于规范误差造成的。从图1可以明显看出，线性模型可能不是一个很好的因变量规范。

assignment代写：变量描述性统计

Table 2 shows the descriptive statistics of all the variables. A total of 10203 observations are taken for the purpose of analysis. The data contains 45.9% males. The average education of the households is 12.851 years. The mean income of the households is CAD 37094.805. To ensure scalability and to make the data smoother, log of income is used instead of income. The average of the log of income is 10.296 and the standard deviation is 0.984. 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. 12% of the households receive social assistance, 13.7% respondents are from rural areas and 61.7% respondents are married. The average age of the respondents is 48.722 years.

assignment代写：变量描述性统计
Figure 1 shows the distribution of the amount spent on recreational expenses. To ensure that the dependent variable is smooth and there are fewer outliers, log of the recreational expenditure is used instead of using the variable directly. The average of log of recreational expenditure is 7.996 with a standard deviation of 1.502. It is evident that the data is smooth and there are no outliers.Table 3 shows the regression results of model where the dependent variable is the log of recreational expenses. The explanatory power of the model is 38.8% and the model is significant at 1% level of significance. The presence of multicollinearity is less likely in the model because in the presence of multicollinearity, the variables lose their statistical significance and the explanatory power of the model increases very strongly (Greene, 2000). This, however, does not hold for this model. The model can be heteroskedastic because of specification error. The presence of heteroskedasticity is often found due to specification error. It is evident from figure 1 that a linear model might not be a good specification for the dependent variable.