Question

Describe an application of exploratory factor analysis that is specific to your industry or to your academic interests(Data Science). Explain why this technique is suitable in terms of measurement scale of variables and their roles.

Answer 1

def:

1)Multivariate statistical methods are used to analyze the joint behavior of more than one random variable.

or

2)Multivariate data analysis by means of projection methods overcomes challenges associated with such applications, such as multidimensionality of the data set

explanation:

Methods dealing with more than one variable at once are called multivariate methods.. These methods can afford hidden data structures.multivariate analysis is a tool to find patterns and relationships between several variables simultaneously. It lets us predict the effect a change in one variable will have on other variables. Multivariate techniques are used to study data sets in consumer and market research, quality control and quality assurance, process optimization and process control, and research and development. These techniques are particularly important in social science research because social researchers are generally unable to use randomized laboratory experiments, like those used in medicine and natural sciences. Here multivariate techniques can statistically estimate relationships between different variables, and correlate how important each one is to the final outcome and where dependencies exist between them.

Determine Appropriate Statistical Method

Because most data analysis tries to answer complex questions involving more than two variables, these questions are best addressed by multivariate techniques. There are several different multivariate techniques to choose from, based on assumptions about the nature of the data and the type of association under analysis. Each technique tests the theoretical models of a research question about associations against the observed data. The theoretical models are based on facts plus new hypotheses about plausible associations between variables.

Advantages of Multivariate Analysis

Multivariate techniques allow researchers to look at relationships between variables in an overarching way and to quantify the relationship between variables. They can control association between variables by using cross tabulation, partial correlation and multiple regressions, and introduce other variables to determine the links between the independent and dependent variables or to specify the conditions under which the association takes place. Advantages of multivariate analysis include an ability to glean a more realistic picture than looking at a single variable. Further, multivariate techniques provide a powerful test of significance compared to univariate techniques.

Example 1. A researcher has collected data on three psychological variables, four academic variables (standardized test scores), and the type of educational program the student is in for 600 high school students. ... A doctor has collected data on cholesterol, blood pressure, and weigh