- Data with multiple variables for each observation - Example: Customer dataset with age, income, and spending score - Used in marketing, finance, healthcare, and social sciences

1. Multivariate Data Analysis &
Factor Analysis
Session 1 - Introduction & Hands-on
Practice

2. What is Multivariate Data?
• - Data with multiple variables for each
observation
• - Example: Customer dataset with age,
income, and spending score
• - Used in marketing, finance, healthcare, and
social sciences

3. Why Multivariate Analysis?
• - Real-world data is complex and
multidimensional
• - Helps uncover patterns and relationships
• - Used for segmentation, prediction, and
classification

4. Types of Multivariate Analysis
• - Factor Analysis: Data reduction
• - Regression Analysis: Predictive modeling
• - Discriminant Analysis: Classification
• - Cluster Analysis: Segmentation

5. What is Factor Analysis?
• - Statistical technique to reduce
dimensionality
• - Identifies hidden relationships between
variables
• - Example: Grouping customer satisfaction
survey questions into key factors

6. Steps in Factor Analysis
• 1. Collect Data
• 2. Check Factorability (Bartlett’s Test, KMO
Test)
• 3. Extract Factors (PCA, MLE)
• 4. Rotate Factors (Varimax Rotation)
• 5. Interpret Results (Factor Loadings, Scree
Plot)

7. Hands-on Factor Analysis
• - Perform Factor Analysis using:
• 1. Excel
• 2. SPSS
• 3. Python (PCA)
• - Identify key factors from a dataset
• - Interpret the results using Scree Plot &
Factor Loadings

8. Real-World Applications of Factor
Analysis
• - Marketing: Identifying key factors in
customer behavior
• - Finance: Risk assessment and investment
analysis
• - Healthcare: Grouping symptoms to diagnose
diseases
• - Social Sciences: Personality trait
identification

9. Classroom Activity
• - Work in groups with a dataset
• - Perform Factor Analysis using a tool of choice
• - Identify key factors and present findings
• - Discuss interpretations with the class

10. Summary & Key Takeaways
• - Multivariate Analysis helps understand
complex datasets
• - Factor Analysis is useful for dimensionality
reduction
• - Hands-on experience with Excel, SPSS, or
Python
• - Applications in marketing, finance,
healthcare, and more