Statistical significance was analyzed by chi-square test.
进行统计学卡方检验分析。
Statistical analysis was performed with the chi-square test.
采用卡方检验进行统计学分析。
Comparison of proportion and correlation analysis was evaluated by Chi-square test.
采用卡方检验进行构成比的比较和相关性分析。
This paper proposes a new future selection algorithm based on information gain and chi-square test.
本文提出了一个基于信息增益和卡方检验的属性选择算法。
This setting is used to find the location (or critical value) on the Chi Square sampling distribution that includes a tail area equal to the alpha-cutoff value (0.05).
该设置用于查找 X平方分布的抽样分布中包含尾数区域等于alpha 断开值（0.05）的位置（或临界值）。
To find the sampling distribution of the Chi Square statistic, simply take the outcome of each experiment and compute a Chi Square statistic for that result.
为了找到X平方分布统计的抽样分布，只需获取每次实验的结果，并且计算该结果的x平方分布统计。
Towards this end, this class contains two methods for displaying the results of the Chi Square analysis.
为了达到这个目的，该类包含了两个用于显示X平方分布分析结果的方法。
So, as you add response options, the statistical probability of obtaining a large Chi Square value should increase and the probability of obtaining smaller Chi Square value decreases.
因此，当您增加回答选项时，获得大的x平方分布值的统计概率应该增加，而获得较小x平方分布值的概率会减少。
Another interesting application of the one-way Chi Square test is to repoll to see if responses have changed.
单向X平方分布检验的另一个有趣应用是重新进行民意测验，以了解人们的回答是否已发生变化。
This is performed in the Chi Square program that I discuss later in this article.
我在本文后面讨论的X平方分布程序中会采用这种做法。
Just to confirm this conclusion, you can compare the obtained Chi Square value to the Critical value.
为了确认这一结论，可以用获得的x平方分布值与临界值进行比较。
The Constructor: Backbone of the Chi Square test
构造函数：X 平方分布检验的主干
The purpose of this wrapper script is to separate the logic of the Chi Square procedure from its presentational aspects.
这个包装器脚本的目的是使x平方分布过程的逻辑与它的表示方面相分离。
The class implements only the Chi Square test logic.
该类只实现X平方分布检验逻辑。
The ChiSquare1D.php class needs access to the Chi Square methods in Distribution.php to compute the tail probability of an obtained Chi Square value.
php类必须能够访问Distribution.php中的 X平方分布方法，以计算所得到的 X平方分布值的尾数概率。
Finally, I demonstrated how to simulate the Chi Square sampling distribution for different degrees of freedom, going beyond simply commenting on its derivation.
最后，我演示了如何模拟不同自由度的x平方分布的抽样分布，而不只是仅说明其来源。
This means that the probability of observing a Chi Square value as extreme as 9.80 under the null hypothesis is 2 percent (which is quite a low probability).
这意味着，在虚假设条件下，观察到X平方分布极限值 9.80的概率是 2％（这是一个相当低的概率）。
As you study these Chi Square graphs, note that the shape of the probability functions change when you vary the degrees of freedom, or df, in your experiment.
当您研究这些X平方分布图时，请注意，当您在实验中改变自由度(即df)时，概率函数的形状会改变。
To find this probability, you need to understand what the sampling distribution for Chi Square looks like.
要得出这一概率，需要理解X平方分布的抽样分布是什么样的。
The final four lines do the bulk of the work in obtaining the Chi Square statistics you are most interested in.
最后四行执行大量的获取X 平方分布统计的工作，这些统计是您最感兴趣的。
The following script writes the obtained Chi Square statistic from each experiment to an output file for later plotting.
下面的脚本将每次实验获得的X平方分布统计写到一个输出文件以便稍后用图表表示。
Look at the Chi Square sampling distribution.
观察x平方分布的抽样分布。
It includes the degrees of freedom used in the analysis and the obtained Chi Square value is reported again.
它包括了分析中使用的自由度，并再次报告了获得的x平方分布值。
In the next few sections, I focus on explaining how the Chi Square software used in this simulation works.
在下面几节中，我将侧重于说明这个模拟实验中所使用的x平方分布软件的工作原理。
The obtained Chi Square value is re-expressed as a tail probability value -- in this case, 0.02.
获得的X平方分布值被重新表示成尾数概率值 —在本例中是 0.02。
In practice, I don't use such graphs to compute tail probabilities because I can implement mathematical functions to return the tail probability for a given Chi Square value.
实际上，我不使用这些图来计算尾数概率，因为我可以实现数学函数来返回给定x平方分布值的尾数概率。
Listing 4 looks at the Chi Square constructor code which forms the backbone of the Chi Square test.
清单4给出了X平方分布的构造函数代码，它构成了X平方分布检验的主干。
The left axis shows the probability, or relative frequency of occurrence, of various Chi Square values.
纵轴显示各X平方分布值的概率（或称为出现的相对频率）。
In general, the probability of obtaining a large Chi Square value goes down as you increase the number of response options in your study.
通常，当您在实验中增加回答选项的数目时，获得较大x平方分布值的概率会下降。
In general, the probability of obtaining a large Chi Square value goes down as you increase the number of response options in your study.
通常，当您在实验中增加回答选项的数目时，获得较大x平方分布值的概率会下降。
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