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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The answer is pyruvate. In the absence of oxygen in
anaerobic respiration, the pyruvate is converted to lactic acid in animals and
ethanol in plants. However, in the presence of oxygen, the pyruvate enters the Krebs
cycle after being converted to Acetyl CoA. Pyruvate is an intermediate product
of glycolysis.
Answer:
The meaningful differences between organisms in a population are genetic. Variations in the genome of members of a population arise through mutation. Occasionally, a mutation occurs in an individual that is beneficial, that helps that organism be better able to survive and repoduce in its current environment.
