RITIK KAUSHAL
21MBA3203
Question 1
What is Factor Analysis and explain the types of Factor Analysis with various applications?
Factor Analysis
A collection of variables is transformed into a new set of composite variables that are
unrelated to each other using factor analysis. In the case of dependency, the predictorcriterion connection is replaced by a matrix of inter-correlations among various variables,
none of which is reliant on another.
Confirmatory factor analysis (CFA)
is a various statistical process for determining if measured variables accurately indicate the
number of components. Confirmatory factor analysis (CFA) and exploratory factor analysis
(EFA) are comparable procedures, however, in EFA, data is simply examined and information
on the number of factors needed to represent the data is provided. All measurable variables are
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connected to every latent variable in exploratory factor analysis. Researchers can define the
number of factors necessary in the data and which measurable variable is associated with
which hidden variable in confirmatory factor analysis (CFA). Confirmatory factor analysis (CFA)
is a method for confirming or disproving a measurement hypothesis.
Exploratory factor analysis
is a statistical approach for condensing data into a smaller number of summary variables and
exploring the phenomena's underlying theoretical structure. It's utilized to figure out how the
relationship between the variable and the respondent is structured. The following two
approaches can be used to do exploratory factor analysis:
Application of factor analysis in business
Investing Factor Analysis- Assume you're a stock trader looking to invest in a new initial public
offering (IPO) for a technological business. IPOs are inherently risky, and there's no way of
knowing whether or not investing in one will provide a return. Investing is a high-risk endeavor
that heavily relies on data analytics. Diversifying one's portfolio reduces risk, but it doesn't
remove it, and it surely doesn't guarantee a return. The investment business can be harmed by
a variety of factors, including macro and microeconomic shocks, natural catastrophes, and so
on. You're probably aware of the advantages of factor analysis at this point. Factor analysis
would predict movement and uncover information that would otherwise go undetected. Imagine
you invested in the IPO along with another investment in a commodity-based company.
A change in the price of a variable that is unrelated, such as petrol, may not be properly
equated without factor analysis. Following a thorough exploratory factor analysis.
Factor Analysis in Market Research Surveys - Assume you're a marketer
doing market research using surveys to gauge customer satisfaction with a product.
Satisfaction is difficult to quantify, and determining what elements contribute to it is
much more difficult. Factor analysis has been utilized in the marketing field to create
things like perception maps and sophisticated SWOT assessments, among other things.
It collects unobservable characteristics like customer satisfaction and compares their
values to observable ones like improved sales. You'd create a questionnaire with
questions on customer satisfaction as the emphasis. This would include aspects like
product durability, packaging, and the possibility of reuse.
Following the completion of the surveys by the consumers, you would do a factor analysis to
extract the variance of each variable and determine which component has the greatest impact
on customer happiness. If you notice that the answers to several variables/questions are
significantly connected, you should combine them into a single factor for future analysis.
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Question 2
Use this dataset for applying a Linear Regression Analysis in SPSS. The
Dependent Variable is Sales. The rest of the Variables are Independent Variables.
1: Run Regression Analysis.
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2 Obtain Model Summary
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3 Make a chart also for the regression line.
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