Unraveling Multimodality with Large Language Models.pdf
Aed1222 lesson 2
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
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.
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
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
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)
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.
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.
Any data you collect yourself falls under the ‘primary source’ category. On the other hand, data made available in statistical reports, text books, and so on are considered secondary source data…
The four levels of measurement will be discussed in later slides under the title of scales of measurement…
You can ask the students before showing the labels: How many variables can you identify here? Three. The amount of Co2 on the y-axis, the year on the x-axis, and the countries or regions. Then you can ask about the type of data for each (whether quantitative or qualitative)… then we show the labels.. This way they will be more involved in the class…
Students should attempt to answer the question before revealing the answer…
The second column ‘mark relative to 40% pass mark’ = mark out of 100 - 40
