Приклади вживання Statistical tests Англійська мовою та їх переклад на Українською
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But, these statistical tests were ignorant of how the data was created.
Methods of verifying statistical hypotheses are called statistical tests.
According to our statistical tests, Mr. Berezenko appears to have a clean victory.
For more on why large datasets render statistical tests problematic, see M.
Statistical tests such as these are particularly important when the testing is relatively expensive.
Lack of quantitative calculations, statistical tests, and experimentation is also a major factor.
In order to achieve it,we analyzed 18,000 responses in our online test and made several statistical tests.
Statistical tests use data from samples to assess, or make inferences about, a statistical population.
The randomness assumption is critically important for the following three reasons: Most standard statistical tests depend on randomness.
Statistical tests indicated that the number of thoughts about sex was not statistically larger than the number of thoughts about food and sleep.
As the results don't capture the same people over time, statistical tests are applied to control for the differences between the two samples.
For example, Ripley defines most probabilistic modeling as stochastic simulation, with MonteCarlo being reserved for Monte Carlo integration and Monte Carlo statistical tests.
There was so much data andthe pattern was so clear that all the statistical statistical tests suggested that this was a real pattern.
This is done by performing many statistical tests on the data and only reporting those that come back with significant results.[1].
Knowledge of advanced statistical techniques and concepts(regression,properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.
(1) No statistical tests performed due to low frequency by treatment and placebo groups as there are limited data in patients treated in the shoulder muscles.
Often these approaches can be used to derive closed-form formulae for statistical tests when direct use of maximum likelihood requires iterative numerical methods.
The biggest problem is that each response option on the scale contains different words, and so it is difficult to place the responses on anevenly spaced mathematical continuum in order to conduct statistical tests.
For more on why large datasets, render statistical tests problematic, see Lin, Lucas, and Shmueli(2013) and McFarland and McFarland(2015).
Silver rejects much ideology taught with statistical method in colleges and universities today, specifically the'frequentist" approach of Ronald Fisher,originator of many classical statistical tests and methods.
In other words, while a PRNG is only required to pass certain statistical tests, a CSPRNG must pass all statistical tests that are restricted to polynomial time in the size of the seed.
All statistical tests have values built in through the choice of numerical"confidence limits", and the management of"outlier" data calls for judgements that can sometimes approach the post-normal in their complexity.
A close examination suggests that"there may be no information in seismic gaps about the time of occurrence orthe magnitude of the next large event in the region";[17] statistical tests of the circum-Pacific forecasts shows that the seismic gap model"did not forecast large earthquakes well".[18] Another study concluded that a long quiet period did not increase earthquake potential.[19].
The smaller the p-value, the more significant the statistical test.
If a statistical test is conducted in a study, false positives and false negatives can be controlled, or at least, evaluated.
We would need atleast a few dozen such historical experiences for a valid statistical test.
This typically creates a multiple testingproblem because each potential analysis is effectively a statistical test.
If we apply a statistical test for independence with a significance level of 0.05 it means there is only a 5% chance of accepting a rule if there is no association.
A statistical test such as chi-squared on the residuals is not particularly useful.[25] The chi squared test requires known standard deviations which are seldom available, and failed tests give no indication of how to improve the model[10].