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Related Concept Videos

Chi-square Distribution01:10

Chi-square Distribution

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How does one determine if bingo numbers are evenly distributed or if some numbers occurred with a greater frequency? Or if the types of movies people preferred were different across different age groups or if a coffee machine dispensed approximately the same amount of coffee each time. These questions can be addressed by conducting a hypothesis test. One distribution that can be used to find answers to such questions is known as the chi-square distribution. The chi-square distribution has...
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Chi-square Analysis02:46

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The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
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Test for Homogeneity01:23

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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
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Finding Critical Values for Chi-Square01:18

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Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...
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Introduction to Test of Independence01:21

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In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
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The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
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Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
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Chi-square test under indeterminacy: an application using pulse count data.

Muhammad Aslam1

  • 1Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, 21551, Saudi Arabia. aslam_ravian@hotmail.com.

BMC Medical Research Methodology
|October 1, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces novel Chi-square tests for K counts within neutrosophic statistics. These tests offer improved analysis of count data, especially under uncertainty, aiding medical decisions.

Keywords:
Chi-square testClassical statisticsCount dataPulse countUncertainty

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Area of Science:

  • Statistics
  • Neutrosophic Statistics
  • Biostatistics

Background:

  • Count data arises from counting processes, which can occur simultaneously or at different times.
  • The Chi-square test for K counts is a traditional method for assessing significant differences in such data.

Purpose of the Study:

  • To develop and present Chi-square tests for K counts under the framework of neutrosophic statistics.
  • To propose test statistics for scenarios where K counts are recorded concurrently or sequentially.

Main Methods:

  • Development of Chi-square test statistics tailored for neutrosophic environments.
  • Application of the proposed testing procedure to analyze pulse count data.

Main Results:

  • The study proposes test statistics for K counts under neutrosophic statistics for both same-time and different-time recording scenarios.
  • Analysis of pulse count data demonstrated the utility of the proposed tests.

Conclusions:

  • The proposed neutrosophic Chi-square test provides valuable insights for cardiologists regarding patient treatment strategies.
  • The developed test offers superior information compared to traditional methods when dealing with uncertainty in count data.