전문 번역가, 번역 회사, 웹 페이지 및 자유롭게 사용할 수 있는 번역 저장소 등을 활용합니다.
this statistical method examines the hypothesis that the sample standard deviation and sample mean value for an emission measured with the candidate system do not differ from the sample standard deviation and sample mean value for that emission measured with the reference system.
Этот статистический метод позволяет проверить правильность допущения, в соответствии с которым стандартное отклонение параметров пробы и среднее значение параметров пробы соответствующих выбросов, измеренных с помощью рассматриваемой системы, не отличаются от стандартного отклонения параметров пробы и среднего значения параметров пробы этих же выбросов, измеренных с помощью эталонной системы.
"sample " means a specially prepared piece of glazing representative of a finished product or a piece cut from a finished product.
2.33 "Образец " означает специально подготовленный фрагмент стеклового материала, являющийся репрезентативным по отношению к готовому изделию, или фрагмент, вырезанный из готового изделия.
getting the right kind of diversity in your sample means making sure that your sample has a range of ages, races, genders, and geographic regions, just to name a few
Обеспечить адекватное многообразие выборки - значит убедиться, что выборка включает людей разных возрастов, рас, полов, географических регионов, не говоря о многом другом
for example, the z-test (ztest) and the t-test (ttest) both assume that the data are independently sampled from a normal distribution. statistics toolbox functions are available for testing this assumption, such as chi2gof, jbtest, lillietest, and normplot. both the z-test and the t-test are relatively robust with respect to departures from this assumption, so long as the sample size n is large enough. both tests compute a sample mean , which, by the central limit theorem, has an approximately normal sampling distribution with mean equal to the population mean μ, regardless of the population distribution being sampled. the difference between the z-test and the t-test is in the assumption of the standard deviation σ of the underlying normal distribution. a z-test assumes that σ is known; a t-test does not. as a result, a t-test must compute an estimate s of the standard deviation from the sample. test statistics for the z-test and the t-test are, respectively, under the null hypothesis that the population is distributed with mean μ, the z-statistic has a standard normal distribution, n(0,1). under the same null hypothesis, the t-statistic has student's t distribution with n – 1 degrees of freedom. for small sample sizes, student's t distribution is flatter and wider than n(0,1), compensating for the decreased confidence in the estimate s. as sample size increases, however, student's t distribution approaches the standard normal distribution, and the two tests become essentially equivalent. knowing the distribution of the test statistic under the null hypothesis allows for accurate calculation of p-values. interpreting p-values in the context of the test assumptions allows for critical analysis of test results. assumptions underlying statistics toolbox hypothesis tests are given in the reference pages for implementing functions.
마지막 업데이트: 2013-05-22
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