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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.
Simple Random Sampling In Simple Random Sampling each possible sample has an equal chance of being selected. Further, each item in the entire population also has an equal chan
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history of north west corner method
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The box plot displays the diversity of data for the totexp; the data ranges from 30 being the minimum value and 390 being the maximum value. The box plot is positively skewed at 1.
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