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:
it's a piece of cake
Explanation:
C. haploid + haploid = diploid
Examples of Newton's 2nd Law, If you use the same force to push a truck and push a car, the car will have more acceleration than the truck, because the car has less mass. It is easier to push an empty shopping cart than a full one, because the full shopping cart has more mass than the empty one.
