By understanding the different types of numerical data and their properties, we can make informed decisions about the appropriate statistical methods to use in analyzing the data. This can help us draw accurate conclusions and make sound decisions based on the data at hand. Therefore, it is important to have a good grasp of the different types of numerical data and their characteristics.

1. Click to edit Master title style
1
M U N A L U L A M U N A L U L A K I N G S T O N E .
PART 1.0 TYPES OF NUMERICAL DATA

2. Click to edit Master title style
2
NOMINAL DATA
2
Nominal data is categorical data where there is no
intrinsic ordering or ranking of the categories.
Examples of nominal data include gender, race,
nationality, and hair color. Nominal data can be
represented as frequencies or percentages, but
mathematical operations such as addition and
subtraction are not meaningful.

3. Click to edit Master title style
3
ORDINAL DATA
3
Ordinal data is categorical data where there is a clear
ordering or ranking of the categories. Examples of
ordinal data include socioeconomic status, level of
education, and customer satisfaction ratings. Ordinal
data can be represented as ranks or levels, but the
distance between the categories is not necessarily equal.

4. Click to edit Master title style
4
RANKED DATA
4
Ranked data is a specific type of ordinal data
where the categories are ordered and ranked, but
the distances between the categories are
unknown or not meaningful. Examples of
ranked data include rankings of sports teams,
ranking of universities, and ranking of job
candidates.

5. Click to edit Master title style
5
DISCRETE DATA
5
Discrete data is numerical data that can only take on certain
values within a finite or countable range. Examples of discrete
data include the number of cars in a parking lot, the number of
students in a class, and the number of books on a shelf.
Discrete data can be represented as a count or a frequency
distribution.

6. Click to edit Master title style
6
CONTINUOUS DATA
6
Continuous data is numerical data that can take on any value
within a continuous range. Examples of continuous data include
height, weight, temperature, and time. Continuous data can be
represented as a range of values or a probability distribution.
Mathematical operations such as addition, subtraction,
multiplication, and division are meaningful for continuous data.

7. Click to edit Master title style
7
SOURCES:
Statistics for Managers Using Microsoft Excel" by David M. Levine,
David F. Stephan, and Kathryn A. Szabat
Statistics: The Art and Science of Learning from Data" by Alan
Agresti and Christine Franklin
ntroduction to Statistical Learning" by Gareth James, Daniela
Witten, Trevor Hastie, and Robert Tibshirani
Practical Statistics for Data Scientists: 50 Essential Concepts" by
Peter Bruce and Andrew Bruce
Statistical Methods for the Social Sciences" by Alan Agresti and
Barbara Finlay

8. Click to edit Master title style
8
MESSAGE TO THE READER
8
THE PRESENATION CAN
BE USED WITH OTHER
ARTICLES, BOOKS etc.,
FOR TYPICAL EXAMPLES

9. Click to edit Master title style
9
THANK YOU