chi-square test oor Grieks
chi-square test
en
(mathematics) any statistical hypothesis test in which the test statistic has a chi-square distribution if the null hypothesis is true
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In cryptanalysis, chi-squared test is used to compare the distribution of plaintext and (possibly) decrypted ciphertext.
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Pearson s chi-square test of difference between placebo and eptifibatide
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A lower bound greater than # % demonstrates non-inferiority between Aloxi and comparator. c Chi-square test
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A chi-squared test can be used to attempt rejection of the null hypothesis that the data are independent.
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The F test and chi square tests are both adversely affected by non-normality and are not recommended for this purpose.
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If a chi squared test is conducted on a sample with a smaller size, then the chi squared test will yield an inaccurate inference.
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*Pearson chi-square test or Exact test ** Duration of best response *** log-rank test stratified by Rai group (Stage I # vs # IV) **** by #-colour flow
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Statistics portal Contingency table Chi-squared test nomogram G-test Minimum chi-square estimation Nonparametric statistics The Wald test can be evaluated against a chi-square distribution.
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A Hazard ratio of less than # favors docetaxel + cisplatin + #-FU *Cox model (adjustment for Primary tumor site, T and N clinical stages and PSWHO) **Logrank test *** Chi-square test
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It is the most widely used of many chi-squared tests (e.g., Yates, likelihood ratio, portmanteau test in time series, etc.) – statistical procedures whose results are evaluated by reference to the chi-squared distribution.
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A Hazard ratio of less than # favors docetaxel + cisplatin + fluorouracil *un-adjusted log-rank test **un-adjusted log-rank test, not adjusted for multiple comparisons ***Chi square test, not adjusted for multiple comparisons NA-not applicable
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Accordingly, and consistent with the Mantel-Haenszel Chi-squared test, is a proposed fix whereby one reduces the error bounds for the two means by multiplying them by the square root of 1⁄2 (0.707107) before making the comparison.
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To reduce the error in approximation, Frank Yates suggested a correction for continuity that adjusts the formula for Pearson's chi-squared test by subtracting 0.5 from the absolute difference between each observed value and its expected value in a 2 × 2 contingency table.
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When the data do not follow a normal distribution, it can still be possible to approximate the distribution of these test statistics by a normal distribution by invoking the central limit theorem for large samples, as in the case of Pearson's chi-squared test.
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Also considered a chi-squared test is a test in which this is asymptotically true, meaning that the sampling distribution (if the null hypothesis is true) can be made to approximate a chi-squared distribution as closely as desired by making the sample size large enough.
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ge a: p value based on Cochran-Mantel Haenszel Chi square test. b: p value based on chi-square test. c: subjects < # kg received # μg/week pegylated interferon alfa-#b and subjects # kg received # μg/week pegylated interferon alfa-#b. lon d: ribavirin dosing was # mg for patients patients # kg
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The p-value was first formally introduced by Karl Pearson, in his Pearson's chi-squared test, using the chi-squared distribution and notated as capital P. The p-values for the chi-squared distribution (for various values of χ2 and degrees of freedom), now notated as P, was calculated in (Elderton 1902), collected in (Pearson 1914, pp. xxxi–xxxiii, 26–28, Table XII).
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One test statistic that follows a chi-squared distribution exactly is the test that the variance of a normally distributed population has a given value based on a sample variance.
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Chi square tests were applied to investigate the possible statistical significant correlations.
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The Chi-square test was used to find relationships between categorical variables.
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A chi-squared test can be used to attempt rejection of the null hypothesis that the data are independent.
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The level of statistical significance for differences in intentions between men and women was estimated according to Chi-Square test.
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The F test and chi square tests are both adversely affected by non-normality and are not recommended for this purpose.
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The chi-squared test was used to compare the case and the control groups with an allelic and a genotype-based analysis.
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