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.
To Learn more about factor analysis from the given link
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Assuming that the trait of being able to taste the sample is a dominant trait, then the tasters have at least one of that dominant trait and the people who were not tasters had the homozygous genes for the recessive trait. Therefore, the answer is
10 - 30
Answer:the virus seems to keep changing and adapting
Explanation:
For example they have found that it now attacks your kidneys heavily causing failure when in the beginning it did not
Contraction is the answer
In experiments with phosphorus and sulfur, both of which burned readily, Lavoisier showed that they gained weight by combining with air. With lead calx, he was able to capture a large amount of air that was liberated when the calx was heated. To a suspicious Lavoisier, these results were not explained by phlogiston
