美国论文代写:经济学定量方法

26 2月 美国论文代写:经济学定量方法

美国论文代写:经济学定量方法

该图清楚地显示数据是正向偏斜的,因为它朝向数据的左侧。样本的范围是40,最小值是0,最大值是40.最小值和最大值的平均值是20.因为数据的峰值小于20,所以可以安全地推断样本是积极倾斜的。但是,为了确认这一点,可以根据表1获得数据的偏度为1.96。积极倾斜的数据也具有这样的性质,即它们的平均值大于它们的中值,这反过来又大于模式。在这里,平均值是6.63,中位数是2.5,模式是0.显然,
意味着>中值>模式
所以,数据是积极倾斜的。

美国论文代写:经济学定量方法
数据的标准偏差很高,为9.07。这意味着数据显示出高度的可变性。这意味着数据在响应之间变化很大。类似的分散度量度是四分位间距,这是第一和第三四分位数之间的差异。如果第一和第三四分位数之间的差异很大,则意味着数据的离散或扩散非常高。在给定的样本中,结果是9,这非常接近标准偏差。
有三种集中趋势测量方法,平均数,中位数和模式。平均值受到极端值的影响,有时可能会有所偏差。另一方面,中间值给出了将数据分成两半的精确点。它不受极端值的影响。模式虽然不受极端值的影响,但却是最高出现频率的数据。在给定的情况下,由于标准偏差很高,数据的传播非常高。所以,意思可能不是一个很好的衡量集中趋势的方法。这里模式也不合适。因此,集中趋势的最佳度量是给出将样本分成两半的近似数据的中位数。

美国论文代写:经济学定量方法

The graph clearly shows that the data is positively skewed because it peaks towards the left of the data. The range of the sample is 40 with minimum being 0 and maximum being 40. The average of the minimum and maximum turns out to be 20. Since, the peak of the data is at less than 20, it is safe to conclude that the sample is positively skewed. However, to confirm this, the skewness of the data can be obtained which according to Table 1 is 1.96. Positively skewed data also have the property that their mean is more than their median which in turn is more than the mode. Here, mean is 6.63, median is 2.5 and mode is 0. Clearly,
Mean>Median>Mode
So, the data is positively skewed.

美国论文代写:经济学定量方法
The standard deviation of the data is quite high which is 9.07. This means that data shows high variability. It implies that data changes a lot between responses. A similar measure of dispersion is the interquartile range which is the difference between the first and the third quartile. If the difference between the first and the third quartile is high, it implies that the dispersion or the spread of the data is quite high. In the given sample, it turns out to be 9 which is quite close to the standard deviation.
There are three measures of central tendency viz. mean, median and mode. Mean is affected by the extreme values and can be deviating sometimes. Median on the other hand gives the exact point which divides the data into two equal halves. It is not affected by extreme values. Mode though not affected by extreme values, is the frequency of highest occurring data. In the given case, since the standard deviation is high, the spread of the data is very high. So, mean may not be a very good measure of central tendency. Mode is not appropriate here either. The best measure of central tendency thus is the median which gives an approximate data that divides the sample into two equal halves.