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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.
Variance The term variance was used to describe the square of the standard deviation by R.A.Fisher. The concept of variance is highly important in areas where it is possible to
Grid is the set of pairs {1, 2, 3, 4} x {1, 2, 3, 4}. Image is the power set of Grid. An element of Image is a subset of Grid and can be represented by a diagram on a 4 by 4
You are given the differential equation dy/dx = y' = f(x, y) with initial condition y(0 ) 1 = . The following numerical method is also given: where f n = f( x n , y n )
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The Null Hypothesis - H0: β0 = 0, H0: β 1 = 0, H0: β 2 = 0, Β i = 0 The Alternative Hypothesis - H1: β0 ≠ 0, H0: β 1 ≠ 0, H0: β 2 ≠ 0, Β i ≠ 0 i =0, 1, 2, 3
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