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Related Concept Videos

Contingency Table01:29

Contingency Table

A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

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Related Experiment Video

Updated: Jul 10, 2026

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Resampling programs for multiway contingency tables with fixed marginal frequency totals.

Paul W Mielke1, Kenneth J Berry, Janis E Johnston

  • 1Department of Statistics, Colorado State University, Fort Collins, CO 80523-1877, USA. mielke@lamar.colostate.edu

Psychological Reports
|October 26, 2007
PubMed
Summary
This summary is machine-generated.

A new resampling algorithm analyzes multiway contingency tables with fixed totals. This method extends exact, chi-squared, and likelihood-ratio tests for three-way tables.

Related Experiment Videos

Last Updated: Jul 10, 2026

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Area of Science:

  • Statistics
  • Computational Statistics

Background:

  • Analyzing multiway contingency tables is crucial in statistical research.
  • Fixed marginal totals present unique analytical challenges.

Purpose of the Study:

  • To introduce a novel resampling algorithm for multiway contingency tables.
  • To extend established statistical tests to three-way table analysis.

Main Methods:

  • Development of a resampling algorithm tailored for multiway contingency tables.
  • Application of the algorithm to Fisher's exact test, Pearson's chi-squared test, and likelihood-ratio tests.

Main Results:

  • The resampling algorithm effectively handles multiway contingency tables with fixed marginal totals.
  • Demonstrated successful extensions of classical statistical tests to three-way tables.

Conclusions:

  • The proposed resampling algorithm provides a robust method for analyzing complex contingency table data.
  • Facilitates advanced statistical inference in multiway contingency table analysis.