Resulting factors are called Second-order factors
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What is factor analysis?</h3>
- Factor analysis is a statistical approach for describing variability in seen, correlated variables in terms of a possibly smaller number of unobserved variables known as factors.
- It is possible, for example, that fluctuations in six known variables mostly reflect variations in two unseen (underlying) variables.
- Factor analysis looks for such joint fluctuations in response to latent variables that are not noticed.
- Factor analysis may be regarded of as a specific form of errors-in-variables models since the observed variables are described as linear combinations of the possible factors plus "error" terms.
- It may help to deal with data sets where there are large numbers of observed variables that are thought to reflect a smaller number of underlying/latent variables.
- It is one of the most commonly used inter-dependency techniques and is used when the relevant set of variables shows a systematic inter-dependence and the objective is to find out the latent factors that create a commonality.
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Answer:
Parasitism describes a relationship between two organisms where one gets benefit and other get harm.
Explanation:
Parasitism is a type of symbiotic association that is present between two different organisms. In association, one organism gets benefit from the other and the other is damaged. For example, association between mosquitoes and human is parasitism because mosquitoes get benefit in the form of food while human is damaged due to disease cause by mosquito biting.
Answer:
It depends on the experiment I think
Explanation:
Answer:
The light is shifted to the red end of the spectrum, as the wavelengths get longer.<em> </em>
