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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The relative frequency of the marked elks are:
- Male Elk = 7/30
- Female Elk = 4/15
- Adult Elk= 173/450
- Baby Elk = 26/22
<h3>How to determine the relative frequency?</h3>
From the complete question, we have:
- Male = 105
- Female = 120
- Adult = 173
- Baby = 52
- Total = 450
The relative frequency of each is then calculated as:
Male Elk = 105/450
Male Elk = 7/30
Female Elk = 120/450
Female Elk = 4/15
Adult Elk= 173/450
Baby Elk = 52/450
Baby Elk = 26/22
<h3>The number of Elks in the park</h3>
Using the data in (a), we can conclude that:
There are 105 male, 120 female, 173 adult, and 26 baby in the park
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