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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It is important to know the division or phylum of the various plant species growing in your backyard so as to determine whether they will spread and ruin other crops and plants of yours. Not every plant is beneficial, so you need to know which ones to keep, and which to deplant.
Answer: Pulmonary aortic valve
Explanation:
Answer:
<h3>Carbon is released back into the atmosphere when organisms die, volcanoes erupt, fires blaze, fossil fuels are burned, and through a variety of other mechanisms. ... Humans play a major role in the carbon cycle through activities such as the burning of fossil fuels or land development.</h3>
Answer:
something the economy can buy with its own money for own personal use
Explanation:
