The main purpose of EFA is to find out the relationship between variables.
- EFA is an abbreviation for exploratory factor analysis, which is used to determine the link between variables that are assessed.
- Exploratory factor analysis is a statistical tool used in multivariate statistics to find the underlying structure of a rather large set of variables. EFA is a factor analysis approach with the overriding purpose of identifying the underlying connections between measured variables.
- The common factor concept underpins EFA. Manifest variables are expressed as a function of common components, unique factors, and measurement errors in this model. Each distinct component has an effect on only one manifest variable and does not explain relationships between manifest variables. Common factors impact several manifest variables, and "factor loadings" are measurements of a common factor's influence on a manifest variable.
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So you would do
5×4×3= 60
and
5×4=20
so, for a 3 digit code there are 60 possibilities and for a 2 digit code there are 20 possibilities.
Answer:
602.7
7,232.4
Step-by-step explanation:
2009 x 0.30 = 602.7
602.7 x 12 = 7232.4
Answer:
1,3,7,9,21,63,
Step-by-step explanation:
63*1=63
21*3=63
9*7=63
7*9=63
3*21=63
1*63=63
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