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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Using photosynthesis, which consists of a series of chemical reactions that require carbon dioxide and water, and store chemical energy in the form of sugar. Light energy from light drives the reactions. Oxygen is released into the atmosphere as a byproduct of photosynthesis.
<span>This is the equation for photosynthesis: 6 CO2 + 6 H2O → 6(CH2O) + 6 O<span>2</span></span>
Level of the carry out airways from the nose or mouth.
