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Factor analysis (FA) explains variability among observed random variables in terms of fewer unobserved random variables called factors. The observed variables are expressed in terms of linear combinations of the factors, plus "error" terms. Factor analysis originated in psychometrics, and is used in social sciences, marketing, product management, operations research, and other applied sciences that deal with large quantities of data.
Factor analysis is applied to a set of variables to discover coherent subsets that are relatively independent of one another. Variables, correlated with each other and independent of other subsets of variables are combined into factors. Factors, which are generated, are thought to be representative of the underlying processes that have created the correlations among variables.
FA can be exploratory in nature; FA is used as a tool in attempts to reduce a large set ' of variable:: to a more meaningful, smaller set of variables. As FA is sensitive to the magnitude Tolerrelations robust comparisons must be made to ensure the quality of the analysis.
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Advantages It is especially useful in case of open-end classes since only the position and not the values of items must be known. The median is also recommended if th
in a normal distribution with a mean of 85 and a STD of 5, what is the percentage of scores between 75 and 90?
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give me question on mean is the aimplest average to understand and easy to compute
Circles or Pie Diagram: Circles or pie diagrams are alternative to squares. These are used for the same purpose i.e. when the values are differing widely in their magnitude
Grouped Data In order to find the median, the median class is to be first located and then interpolation is to be used by assuming that items are evenly spaced over the entire
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