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:
See answer below. Hope it helps.
Explanation:
This can have advantages and disadvantages. The artificially selected animals could have better traits than naturally selected animals, but in the long run, it will be harder for them to evolve and adapt to new environments because of the lack of variation in their traits.
Answer:
Thus the air temperature is highest near the surface and decreases as altitude increases. ... Therefore, air pressure decreases as we increase altitude. The air density depends on both the temperature and the pressure through the equation of state and also decreases with increasing altitude.
Explanation:
