To identify underlying dimensions, or factors, that explain the correlations among a set of variables.Principal Components Analysis (PCA) is one of the most frequently used techniques. In exploratory factor analysis, specific hypotheses about how many factors will emerge, and what items these factors will comprise are not requires (as opposed to confirmatory factor analysis). No distinction between dependent and independent variables and all variables are considered simultaneously. It is an interdependence technique, meaning that there is Generally, factor analysis is a class of procedures used for data reduction or summarization. In this chapter, we will focus on exploratory factor analysis. 9.2 Assignment 3 (Hypothesis Testing 2).8.1 Random Variables & Probability Distributions.5.6.1 Confidence intervals for proportions.5.1.2 Statistical inference on a sample.4.3.1 Confidence Intervals for the Sample Mean.4 Introduction to Statistical Inference.3.3.1 Creating a new R-Markdown document.2.3.2 Import data created by other software packages.1.1 How to download and install R and RStudio.
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