Look at the Chi Square sampling distribution.
观察x平方分布的抽样分布。
Table 3 reports various Chi Square statistics.
表3报告了各种X平方分布统计信息。
The Constructor: Backbone of the Chi Square test
构造函数：X 平方分布检验的主干
The positive rates in different years were compared by chi square test.
并对每年检查出的阳性例数进行百分数计算并作卡方检验。
When designing and making charts, he sometimes has to use a one chi square.
他设计制图的时候，有时要用到方尺。
In this study, the obtained Chi Square value was larger then the Critical value.
在本文中，获得的X平方分布值大于临界值。
Compare and analyse the data by the means of Frequencies, Crosstabs and Chi square test.
采取了频数分析、列联表分析和卡方检验等方法进行比较分析。
This is why the shape of the Chi Square sampling distribution changes for different df values.
这就是为什么X平方分布的抽样分布的形状随着df值的不同而变化的原因。
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。
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。
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。
This Chi Square statistic will vary from experiment to experiment due to random sampling variability.
由于随机抽样的可变性，因此这个X平方分布统计会随实验的不同而不同。
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）。
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。
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。
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。
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。
The code in Listing 5 shows how easy it is to perform a Chi Square analysis using the ChiSquare1D.php class.
清单5中的代码展示了使用ChiSquare1D.php类执行 X平方分布分析是多么容易。
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。
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。
In each of the graphs, the bottom axis reflects the size of an obtained Chi Square score (range showing is 0 to 10).
在每幅图中，横轴表示所得到的x平方分布值大小(图中所示范围从0到10)。
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 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。
The Chi square distance between corresponding LBP histograms of sample image and template is used to construct weak classifiers pool.
计算样本图像的LBP直方图和模板的LBP直方图之间的度量，并由此构建弱分类器集。
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。
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。
This result object contains all the important details about the test, including three critical Chi Square statistics — ChiSqObt, ChiSqProb, and ChiSqCrit.
这个结果对象包含了有关检验的所有重要详细信息，包括三个重要的X平方分布统计—ChiSqObt、ChiSqProb和ChiS q Crit。
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。
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。
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