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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Two-Way ANOVA01:17

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...
One-Way ANOVA01:18

One-Way ANOVA

One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Introduction to Test of Independence01:21

Introduction to Test of Independence

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.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

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:
H0: The two variables (factors)...

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Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

A multivariate test of association.

Manuel A R Ferreira1, Shaun M Purcell

  • 1Department of Psychiatry, Massachusetts General Hospital, Boston, USA. manuel.ferreira@qimr.edu.au

Bioinformatics (Oxford, England)
|November 21, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new multivariate association test for genetic studies. It is designed for efficient application in large population studies to analyze multiple related traits.

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Area of Science:

  • Genetics
  • Biostatistics

Background:

  • Genetic association studies frequently analyze multiple, related phenotypes.
  • Limited formal multivariate statistical tests are available for such analyses.

Purpose of the Study:

  • To introduce a novel multivariate test of association for genetic studies.
  • To provide an efficient method for analyzing multiple related phenotypes in large datasets.

Main Methods:

  • Development of a new statistical test for genetic association.
  • Focus on efficient application in large population-based study designs.

Main Results:

  • The described test is suitable for efficiently analyzing multiple related phenotypes.
  • The method is applicable to large population-based genetic studies.

Conclusions:

  • The new multivariate association test offers an efficient approach for genetic research.
  • This method addresses the need for formal statistical tests when examining multiple related phenotypes.