The document provides information about collecting and summarizing data. It defines key terms like population, parameter, sample, statistic, descriptive and inferential statistics. It discusses different types of variables like quantitative, qualitative, continuous, discrete, ranked and categorical. Examples are given of different data types and how to collect, organize and present data in tables, graphs like bar graphs and pie charts. The last part contains exercises asking the reader to identify if variables are categorical or numerical, and if numerical, whether they are discrete or continuous.

1. Task
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● lecture
1

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4. Lect 1. Course code: FFT2074
Course Title
BIOMETRY AND
EXPERIMENTAL
DESIGN!
Observed data & their
Characteristics!
Prof Dr Md Ruhul Amin

5. Introduction and Data Collection
1.1 Some definitions!
❑ Statistics: Statistics is a subject of study dealing with the process of
collecting, organizing, summarizing, analyzing and presenting (COSAP)
information.!
❑ Population: Population is the totality of items or things under consideration
possessing certain characteristics of interest.!
❑ Parameter: Parameter (yardstick) is a summary measure that describes or
represent a characteristic of an entire population. !
❑ Sample: Sample is the representative portion of the population that is selected
for analysis.!
❑ A statistic is a summary measure computed from sample data that is used to
describe or estimate a characteristic of the entire population.

6. ….Definitions
Descriptive statistics
Descriptive statistics is the
method that focus on the
collection, presentation and
characterization of a set of data
in order to properly describe the
various features of that set.
Inferential statistics
Inferent ial stat ist ics is the
method of estimating the
characteristics of a population or
t h e m a k i n g o f a
decision concerning a
population based only on sample
results.
e.g. This one e.g. Mean height of SBH male is better than that one
students: 5’

7. Definitions…
!
Variable: a variable is any measured characteristic or attribute that differs for
different subjects. !
!
For example, if the weight of 30 subjects were measured, then weight would be a
variable. !
!
If no. of students in different classes were counted then no. of students counted
would be a variable. !
!
Different classes – also variable.!
!
Census: Counting total no of subject. For example Census of human population in
Malaysia.

8. Biometry
● Statistics applied in the field of Life Science
is called !
BIOMETRY or!
BIOSTATISTICS!
Life Science includes Biological Science, Medical
Science, Agricultural Science

9. Why data are needed?
● Provide the necessary input to a survey!
● Provide the necessary input to a study!
● Measure the performance of an ongoing service or
production process!
● Evaluate the conformance of standards!
● Assist in formulating alternative courses of action
in the decision making process!
● Satisfy our curiosity (eg days required to incubate eggs of
chicken, quail and duck)

10. Observation of a particular event
Generally an observation can be classified as either
QUANTITATIVE or QUALITATIVE. !
Quantitative observations are based on some sort of
measurement or count eg. Length, weight, temperature
and pH, number of balls in the basket. !
Qualitative observations are based on categories reflecting
a quality or characteristics of the observed event; eg. Male
vs female, diseased vs healthy, live vs dead, coloured vs
colourless etc. Any observation when recorded is called
DATA.

11. Types of variable
1. Quantitative variable!
• a. Continuous variable!
• b. Discrete variable
2. Ranked or ordinal variable!
• Example: Voters classified by parties!
• Students classified according to height
3. Categorical or qualitative variable!
• Examples Male vs Female!
• Red vs White

12. Variables or Data types
There are several data types that arise in statistics. Each statistical test
requires that the data analyzed be of a specific type. Most common types
of variables-!
1. Quantitative variables – fall into two major categories!
a) Continuous variables- can assume any value in some
(possibly unbounded) interval of real numbers. Common
examples include length, weight, temperature, volume (milk
production) and height. They arise from MEASUREMENTS.!
b) Discrete variables- assume only isolated values.
Examples include clutch size, trees per hectare, teats per
sow, no. of days per month, no. of patient for a particular
disease in hospitals. They arise from COUNTING.

13. Variables or Data types…
2. Ranked data (ordinal variables) are not measured but
nonetheless have a natural ordering. For example,
candidates for political affiliation can be ranked by individual
voters. Or students can be arranged by height from shortest
to tallest and correspondingly ranked without being
measured. A candidate ranked 2 is not twice as preferable as
the person ranked 1.!
!
3. Categorical data or qualitative data: Some examples are
species, gender (M/F), healthy vs diseased and marital
status (married/ unmarried). Unlike ranked data, there is no
‘natural’ ordering that can be assigned to these categories.

14. 1. Examples of data types
Data type Question type Responses
Numerical How many balls are in
the basket ?
Number
How tall you are? ……. Inches/cm
Categorical 1.Do you have any
work experience?
Yes or No
2. Name the types of
victims in street
accidents!
Killed or injured or
unaffected

15. 2.Example of nominal scaling
Categorical variables Categories
Colour of ball in the basket Blue / Red /Yellow/ Black
Marital status Single / Married /Widow

16. 3. Example of ordinal scaling
Categorical variables Ordered categories
Students grades A B C D E F
Product satisfaction Unsatisfied Neutral Satisfied
Victims of street accident Died / Seriously injured / Slightly
injured / Intact

17. 4. Example of interval and ratio
scaling
Numerical value Level of measurement
Temperature Interval
STANDARD Exam Score Interval
Height, weight, age, salary Ratio

18. Classification of variable
Variable
Qualitative
Example!
Yes/No!
Ranked or Ordinal
Ranking of voters
according to political
affiliation
Quantitative
Continuous
Height of students
Discrete
No of victims in
accident

19. Collecting data
● Primary data - the data that are gathered by researcher
or data collector!
● Secondary data (source data) are the data obtained
from data reservoir/data bank!
Once you have decided to use either secondary data or
primary data or both, the next step is on how to collect
the data. To collect secondary data is not a big problem.
Just to approach the authority. Primary data collection
needs speci f ic design to have accurate and
representative data at a minimum cost and time.

20. Reason for drawing a sample
1. A sample is less time consuming
2. A sample is less costly to administer than a census
3. A sample is less cumbersome and more practical to administer than a
census
Note: A sample must be representative for specific population/subpopulation

21. Table and graphs
● The data collected in a sample are often organized into a
table or graph as a summary representation. The
following table shows the no. of sedge plants found in
800 sample quadrats (1m2 ) in an ecological study of
grasses. Example 1. A frequency distribution table!
Table 1. Plant/
quadrat (x
Frequencies
(fi
Total
1 268
2 316
3 135
4 61 800
5 15
6 5

22. Example 2. Frequency data
The following data were collected by randomly sampling a
large population of rainbow trout. The variability of interest
is weight (lb)
Xi f i fi
1
2 2
2
1 2
3
4 12
4
7 28
5
13 65
Total
27 109

23. Example 2….
● Rainbow trout have weights that can range from almost
if
0-20 lb or more. Moreover their wt.s can take any value
in that interval. For example, a particular trout may
weigh 4.3541 lb. From example 2!
!
!
Σ f X
i 109
● X
= = =
4.037
Σ
27
lb. !
i
! ! ! ! !

24. A sample of bar graph/histogram
Series 3
Series 2
Series 1
Distribution of 3 (series) races in
4 states (category) of Malaysia

25. A sample of bar graph….
● Category!
Categories may be: different
states in Malaysia
● Series !
Series may be people!
1. Malay!
2. Chinese origin!
3. Indian origin

26. Pie chart

27. Line Diagram

28. Example of a chart
Month
2011
Travel
abroad
Exam Plantatio
n
Confere
nce
In Kl In home
JAN x
FEB X X
MAR X X
APR X X
MAY X
JUNE X X
JULY X
AUG X X
SEP x X
OCT X
NOV X X
DEC x X

29. Exercises
1. For each of the following random
variable determine whether the
v a r i a b l e i s c a t e g o r i c a l o r
numerical. If numerical, determine
whether the variable of interest is
discrete or continuous.

30. Exercise 1
No. of telephones per household
Type of telephone primarily used
No. of long-distance call made per month
Length (minute) of long-distance call made per month
Colour
of telephone primarily used
Monthly charge (RM) for long-distance call made
No. of local call made per month
Whether there is a telephone line connected to a computer modem
in the household

31. Thank you
31