In this ppt you can find the materials relating to Application of Univariate, Bivariate and Multivariate Variables in Business Research. Also What is Variable, Types of Variables, Examples of Independent Variables, Examples of Dependent Variables, Common techniques used in univariate analysis include, Common techniques used in bivariate analysis include, Common techniques used in Multivariate analysis include, Difference B/w Univariate, Bivariate & Multivariate Analysis

1. Understanding
Research Variables-
Univariate, Bivariate & Multivariate
By
Sundar B N

2. What is Variable
● a variable is any characteristic, number, or quantity that
can be measured or controlled and that can vary or take on
different values.
● Variables are central to the scientific method as they allow
researchers to systematically study relationships, make
comparisons, and draw conclusions.
Age, business income and
expenses, country of birth,
capital expenditure, class
grades, eye colour and
vehicle type
Examples

3. Dependent variable (DV):
Types of Variables
This is the variable that is
observed and measured for
changes as a result of the
independent variable's
manipulation. It is also known as
the outcome variable or
response variable.
This is the variable that the
researcher manipulates or
controls in an experiment to
observe its effect on the
dependent variable. It is also
known as the predictor variable
or treatment variable.
Independent variable (IV):

4. Examples of Independent Variables:
Marketing expenditure: The amount of money spent on
advertising, promotions, and other marketing activities.
Price of a product or service: The cost at which a product or
service is offered to customers.
Employee training programs: The type and intensity of training
provided to employees.
Leadership style: The approach taken by managers or leaders in
directing and motivating their teams.
Technological innovation: The introduction of new technologies
or processes within a company.

5. Examples of Dependent Variables:
Sales revenue: The total income generated from selling products or
services.
Customer satisfaction: The level of satisfaction or dissatisfaction
experienced by customers with a company's products or services.
Employee performance: The effectiveness and productivity of
employees in achieving organizational goals.
Market share: The portion of total sales within an industry that a
company captures.
Profit margin: The ratio of profit to revenue, indicating the efficiency of
a business in generating profits.

6. Univariate Analysis
Univariate refers to a type of
statistical analysis that
involves the examination
of one variable at a time.
In other words, univariate
analysis focuses on
describing and analyzing
the distribution, central
tendency, and variability
of a single variable
without considering
relationships with other
variables.
Example: Examining the distribution of exam scores for a
class of students.
Data: Scores obtained by each student on an exam.
Analysis: Calculate descriptive statistics such as mean,
median, mode, variance, and standard
deviation to understand the central tendency
and variability of scores.
Create a histogram or frequency distribution to
visualize the distribution of scores.
Objective: To understand the performance of students on
the exam and identify any patterns or outliers
in the scores.

7. Common techniques used in univariate
analysis include
 Descriptive statistics
 Frequency distributions
 Histograms and bar charts
 Box plots
 Measures of variability

8. Bivariate Analysis
Bivariate refers to a type of
statistical analysis that
involves the
examination of the
relationship between
two variables.
Unlike univariate analysis,
which focuses on a
single variable, bivariate
analysis examines how
two variables are
related or associated
with each other.
Example: Investigating the relationship between study
hours and exam scores.
Data: Study hours (independent variable) and exam
scores (dependent variable) for a group of
students.
Analysis: Plot a scatter plot with study hours on the x-axis
and exam scores on the y-axis.
Calculate Pearson's correlation coefficient to
measure the strength and direction of the linear
relationship between study hours and exam
scores.
Objective: To determine if there is a significant correlation
between the amount of time spent studying and
exam performance.
Example

9. Common techniques used in bivariate
analysis include:
 Scatter plots
 Correlation analysis
 Cross tabulation (contingency tables)
 Chi-square test
 Regression analysis

10. Multivariate Analysis
Multivariate analysis involves
the simultaneous analysis of
multiple variables to
understand the
relationships among them.
Multivariate analysis considers
the interactions and
dependencies between
three or more variables.
Multivariate analysis
encompasses a wide range
of statistical techniques,
each suited for different
types of data and research
questions.
Example: Understanding the factors influencing customer
satisfaction in a restaurant.
Data: Customer satisfaction (dependent variable) and
various factors such as food quality, service speed,
cleanliness, and ambiance (independent variables).
Analysis: Conduct multivariate regression analysis with
customer satisfaction as the dependent variable and food
quality, service speed, cleanliness, and ambiance as
independent variables.
Perform factor analysis to identify underlying dimensions
(factors) that explain the correlations among the different
satisfaction factors.
Objective: To identify which factors most strongly influence
customer satisfaction and understand the overall satisfaction
drivers in the restaurant.
Example

11. Some common methods of multivariate
analysis include:
 Multivariate regression analysis
 Principal component analysis (PCA)
 Factor analysis
 Cluster analysis
 Multivariate analysis of variance (MANOVA)
 Canonical correlation analysis (CCA)

12. Difference B/w Univariate, Bivariate & Multivariate Analysis
Basis for
Diff
Univariate Bivariate Multivariate
Focus
Univariate analysis
examines a single variable
at a time
Bivariate analysis examines
the relationship between two
variables
Multivariate analysis involves the simultaneous
analysis of three or more variables.
Objective
The objective is to describe
and understand the
characteristics, distribution,
and variability of the
variable.
The objective is to determine
if there is a relationship,
association, or correlation
between the two variables.
The objective is to understand complex
relationships among multiple variables,
considering interactions and dependencies
between them
Examples
Descriptive statistics such
as mean, median, mode,
variance, standard
deviation; graphical
representations like
histograms, bar charts, and
box plots.
Scatter plots, correlation
analysis (e.g., Pearson
correlation coefficient), chi-
square tests,
crosstabulations, simple
linear regression.
Multivariate regression analysis, principal
component analysis (PCA), factor analysis,
cluster analysis, multivariate analysis of
variance (MANOVA), canonical correlation
analysis (CCA).
Application
Commonly used for
preliminary exploration of
data and understanding the
properties of individual
variables.
Used to explore the
connection between two
variables and understand
how changes in one variable
are related to changes in
another.
Used to uncover patterns, identify underlying
structures, and analyze complex relationships
among multiple variables in data

13. Application of Univariate
analysis in Business
Sales Analysis
Financial Performance
Inventory Management
Customer Behavior Analysis
Marketing Effectiveness
Employee Performance

14. Application of Bivariate
analysis in Business
Sales and Marketing
Price and Demand
Customer satisfaction and
repeat purchases
Interest rates and
investment returns:
Company size and
profitability
Employee Performance &
Productivity

15. Application of Multivariate
analysis in Business
Market Segmentation
Customer Relationship
Management
Product Development and
Improvement
Financial Analysis and Risk
Management
Market Research and
Competitive Analysis
Employee Engagement and
Performance Management

16. Thank you