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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.
The incidence of occupational disease in an industry is such that the workers have a 20% chance of suffering from it. What is the probability that out of six workers 4 or more will
#There were three types of food, and the researcher recorded which foods were bought. Peanut Butter Banana Hamburger 15
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Cause and Effect Even a highly significant correlation does not necessarily mean that a cause and effect relationship exists between the two variables. Thus, correlation does
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Related Positional Measures Besides median, there are other measures which divide a series into equal parts. Important amongst these are quartiles, deciles and percentiles.
what are the challenges affecting population census in developing countries
A study was conducted to determine the amount of heat loss for a certain brand of thermal pane window. Three different windows were randomly subjected to each of three different ou
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