1. Introduction to Statistics for Built
Environment
Course Code: AED 1222
Compiled by
DEPARTMENT OF ARCHITECTURE AND ENVIRONMENTAL DESIGN (AED)
CENTRE FOR FOUNDATION STUDIES (CFS)
INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

2. Statistical Data
Data collection is one of the most critical steps in carrying out a
statistical study.
The quality of data gathered determines the quality of the study.
Sources of Statistical DataSources of Statistical Data
Data that are made
available by others
Data that are made
available by others
Data resulting from
an experiment
Data resulting from
an experiment
Data collected in an
observational study
Data collected in an
observational study
Secondary Source Data Primary Source Data
Examples:
 Survey, Questionaire, Observation
Examples:
 Reports, Books, Catalogue, Brochure

3. Types of Data
Statistical data may be classified into the two main categories of
DATADATA
Qualitative
(Categorical/Attribute)
data, that refers only to name
classifications
Qualitative
(Categorical/Attribute)
data, that refers only to name
classifications
Quantitative
(Numerical)
data that represent counts or
measurements
Quantitative
(Numerical)
data that represent counts or
measurements
Discrete
(Counted items)
Discrete
(Counted items)
Continuous
(Measured characteristics)
Continuous
(Measured characteristics)
Examples:
 Marital Status
 Political Party
 Eye Color
(Defined categories)
Examples:
 Number of Children
 Defects per hour
Examples:
 Weight
 Voltage
Business Statistics: A Decision-
Making Approach, 7e © 2008
Prentice-Hall, Inc.

4. Qualitative Data/Variables
Qualitative Data : Labels or names used to identify an attribute
of each element.
Eye color: dark brown, black, grey…
Exam results: pass or fail.
Socio-economic status: low, middle, high…
Race: Malay, Chinese, Indian…
For example…
It is represented in the form of separate distinct categories.

5. Quantitative Data /Variables
Quantitative Data indicate either “how many” or “how much”.
Data is in the form of counts or measurements.
Data are always numeric.
Some examples of quantitative variables are height,
weight, and shoe size.

6. Qualitative data
Quantitative data
Quantitative data

7. Qualitative data
Quantitative data
Qualitative data

8. Quantitative data
Quantitative data
Qualitative data

9. Discrete & Continuous variables
Quantitative variables can be classified as DISCRETE or
CONTINUOUS
1. A discrete variable is a Variable with possible scores of
discrete points on the scale.
For example…
1.The number of children in a household.
A household could have 3 children or 6 children, but not 4.53 children.
2.The number of rooms in a house.
A house could have 3 rooms or 5 rooms, but not 3.67 rooms.
Why discrete?
Because discrete variables can only be integers or whole
numbers

10. Business Statistics: A Decision-
Making Approach, 7e © 2008
Prentice-Hall, Inc.
Discrete Data
• Discrete data: possible values are countable
Example: An
advertiser asks 200
customers how many
days per week they
read the daily
newspaper.
Number of days
read
Frequency
0 44
1 24
2 18
3 16
4 20
5 22
6 26
7 30
Total 200

11. Discrete vs. continuous variables cont…
2. A continuous variable is where the scale is continuous and
NOT made up of discrete steps, and measurements can take
on any value.
For example: time taken to respond to a question.
The response time could be 1.64 seconds, or it could be
1.64237123922121 seconds.
Another example could be the weight of a person or an object.
A scale might show the weight of the person as 50.5 Kg but a
more accurate scale could show a weight of 50.538 Kg

12. Business Statistics: A Decision-
Making Approach, 7e © 2008
Prentice-Hall, Inc.
Continuous Data
• Continuous Data: may take on any value in
some interval
Example: A manufacturer of insulation randomly selects 20
days and records the daily high temperature
24.5, 35.7, 17.3, 21.4, 24.5, 37.8, 26.6, 46.7, 58.2, 30.4,
32.6, 13.5, 12.7, 38.3, 41.2, 43.7, 44.9, 27.6, 53.8, 27.0
(Temperature is a continuous variable because it could
be measured to any degree of precision desired)

13. 13
“Number of correct answers on a 100
point multiple-choice test”.
(discrete)

14. 14
“Marital status of 50 residents in a
neighborhood”.
(discrete)

15. 15
“Goal scores of each football team in the
FIFA World Cup 2010”.
(discrete)

16. Sem. I, Session 2008/9
AED 1122
Introduction to Mathematics & STATISTICS
for Built Environment
16
(continuous)
“Time taken to reach the class“.

17. 17
“Height of tsunami waves in different
affected countries”.
(continuous)

18. Measurement of variables
VARIABLES
IntervalIntervalNominalNominal
OrdinalOrdinal RatioRatio
The "levels of measurement", or scales of measure are
expressions that typically refer to the theory of scale types
developed by the psychologist Stanley Smith Stevens .
Stevens claimed that all measurement in science was conducted
using four different types of scales that he called "nominal",
"ordinal", "interval" and "ratio".

19. The nominal scale
• Nominal measurement consists of assigning items to groups
or categories.
• No quantitative information is conveyed in nominal data.
• No ordering of the items is implied.
• Nominal scales are used to measure QUALITATIVE variables
only.
• For example… rocks can be generally categorized as igneous,
sedimentary and metamorphic
• Other example… Religious preference, race, and gender.

20. Nominal variable:
• All energy sources (Petroleum, Natural Gas, Coal,
Nuclear Electric & Renewable Energy)

21. Nominal variable:
• 10 poorest countries in the world

22. The Ordinal Scale
 Ordinal measurements are ordered in the sense that higher
numbers represent higher values.
 The intervals between the numbers are not necessarily equal.
 Allow us to rank order the items in terms of “which has less?”
and “which has more?”
 Cannot say “how much more?”
 Examples:- A Likert Scale use names with an order such as:
"bad", "medium", and "good"; or "very satisfied", "satisfied",
"neutral", "unsatisfied", "very unsatisfied."
Other examples.. Socio economic status of families, Level of
education, Gold Silver and Bronze at the Olympics.

23.  Interval is a scale in which a certain distance along the scale
means the same thing no matter where on the scale you are,
but where "0" on the scale does not represent the absence of
the thing being measured.
The Interval Scale
 Allows us not only to rank order the items that are
measured, but also to quantify and compare the sizes of
differences between them.
 For example… temperature, as measured in degrees
Fahrenheit or Celsius, constitutes an interval scale. Equal
differences on this scale represent equal differences in
temperature, but a temperature of 30 degrees is not twice as
warm as one of 15 degrees.

24. The Ratio Scale
 Ratio is very similar to interval variables; in addition to all the
properties of interval variables, it features an identifiable
absolute zero "0" point.
 Another example is the Kelvin temperature. Not only can we
say that a temperature of 200 degrees is higher than one of 100
degrees, we can correctly state that it is twice as high.
 Interval scales do not have the ratio property.
 Typical examples of ratio scales are measures of time or space.

25. Business Statistics: A Decision-
Making Approach, 7e © 2008
Prentice-Hall, Inc.
Data Measurement Levels
Ratio/Interval Data
Ordinal Data
Nominal Data
Highest Level
Complete Analysis
Higher Level
Mid-level Analysis
Lowest Level
Basic Analysis
Categorical Codes ID
Numbers
Category Names
Rankings
Ordered Categories
Measurements

26. A practical example
Student
Mark out of
100%
Mark relative to
40% pass mark
Position Result
Ahmed 56 16 6 Pass
Ali 48 8 7 Pass
Comara 65 25 3 Pass
Dawod 73 33 2 Pass
Elias 62 22 4 Pass
Fatima 35 -5 10 Fail
Sayyed 20 -20 9 Fail
Hana 38 -2 8 Fail
Nurul 58 18 5 Pass
Zaleha 82 42 1 Pass
Ratio Interval Ordinal Nominal

27. Independent & dependant variables
• Independent variables:
– manipulated by the experimenter.
– the subjects/factors.
• Dependant variables:
– measured from the subjects.
– dependant upon the subjects/factors.
In general the independent variable is manipulated by the
experimenter and its effects on the dependent variable are
measured.
For example…

28. An experimenter/researcher might want to compare the
effectiveness of four different types of antidepressants.
In this case, the independent variable (being manipulated) is the
"type of antidepressant used".
The experiment seeks to determine the effect of the
independent variable (the type of medicine) on relief from
depression.
Therefore, in this example, relief from depression is the
dependent variable.

29. Independent Dependent

30. Independent
Dependent

31. Independent
Independent