英語 での Test statistic の使用例とその 日本語 への翻訳
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And the test statistic D is.
When n1≠ n2, there is no test statistic.
A test statistic D is computed.
When n1= n2, the test statistic is Z2.
The test statistic F is given by.
Then we calculate the test statistic(Z), defined by.
The test statistic JB is defined as.
Here we can use the sample count as our test statistic.
In this case the test statistic T is the sample mean.
That is called the critical region of the test statistic.
The test statistic is used to calculate the p-value.
The modified Anderson-Darling goodness-of-fit test statistic is calculated for each distribution.
Dixon's test statistic is denoted by rij, where the subscripts i and j indicate the following.
If you test whether the smallest data value is an outlier,then the test statistic G is given by.
After all, our test statistic doesn't fall in the rejection region.
When the data show strong evidence against the assumptions in the null hypothesis,the magnitude of the test statistic becomes too large or too small depending on the alternative hypothesis.
If the test statistic is between the first and second values, you fail to reject the null hypothesis.
For example, if the calculated p-value of a test statistic is less than 0.05, you reject the null hypothesis.
Grubbs' test statistic(G) is the difference between the sample mean and either the smallest or largest data value, divided by the standard deviation.
You can measure this using a test statistic that has an F-distribution with(k- 1, N- k) degrees of freedom.
The test statistic column shows score statistics for parameters that are not in the current model and shows Wald statistics for parameters that are in the current model.
That is to say, any Chi test statistic higher than twelve gives a 95% confidence about the conclusions.
Because there is no test statistic for the Bonnet method, Minitab uses the rejection regions that are defined by the confidence limits to calculate a p-value.
The distribution of the test statistic under the null hypothesis determines the probability α of a type I error.
The distribution of the test statistic under the alternative hypothesis determines the probability β of a type II error.
Minitab displays a test statistic for each test that has a calculable test statistic.
A sufficiently high test statistic indicates that at least one difference between the medians is statistically significant.
A sufficiently high test statistic indicates that the difference between some of the standard deviations is statistically significant.
The likelihood ratio test statistic is used to compute a control chartstatistic that has an approximate upper control limit of 1.