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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.
Simulation When decisions are to be taken under conditions of uncertainty, simulation can be used. Simulation as a quantitative method requires the setting up of a mathematical
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Objective of index numbers
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give me question on mean is the aimplest average to understand and easy to compute
b. A paper mill produces two grades of paper viz., X and Y. Because of raw material restrictions, it cannot produce more than 400 tons of grade X paper and 300 tons of grade Y
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