Course code: BFT2074 Course Title

BIOMETRY AND EXPERIMENTAL

DESIGNObserved data & their

CharacteristicsProf Dr Md Ruhul Amin

Introduction and Data Collection

1.1 Some definitionsStatistics: Statistics is a 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 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.

Descriptive statistics

Inferential 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 is the method of estimating the characteristics of a population or the making of a decision concerning a population based only on sample results.

….Definitions

e.g. This one is better than that one

e.g. Mean height of SBS students: 5’

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.

Definitions…

Biometry

Statistics applied in the field of Life Science is called

BIOMETRY or BIOSTATISTICSLife Science includes Biological Science,

Medical Science, Agricultural Science

Why data are needed?

Provide the necessary input to a survey

Provide the necessary input to a studyMeasure the performance of an

ongoing service or production processEvaluate the conformance of

standardsAssist in formulating alternative

courses of action in the decision making process

Satisfy our curiosity

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.

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

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 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.

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.

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

2.Example of nominal scaling

Categorical variables Categories

Colour of ball in the basket Blue / Red /Yellow/ Black

Marital status Single / Married /Widow

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

4. Example of interval and ratio scaling

Numerical value Level of measurement

Temperature Interval

STANDARD Exam Score Interval

Height, weight, age, salary Ratio

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 specific design to have accurate and representative data at a minimum cost and time.

Reason for drawing a sample

1.

A

sampl

e i

s l

ess ti

me

consumi

ng

Table and graphsThe 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 (xi )

Frequencies (fi)

Total

0 268

1 316

2 135

3 61 800

4 15

5 3

6 1

7 1

Example 2.

The following data were collected by randomly sampling a large population of rainbow trout. The variability of interest is weight (lb)

Xi (lb) f i fiX I

12 2

21 2

34 12

47 28

513 65

Total27 109

Example 2….

Rainbow trout have weights that can range from almost 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

lb. i

ii

f

XfX

037.4

27109

A sample of bar graph

Category 1 Category 2 Category 3 Category 40

2

4

6

8

10

12

14

Series 3Series 2Series 1

A sample of bar graph….

CategoryCategories may be: 4

different states in Malaysia

Series Series may be people1. Bumi putra2. Chinese origin3. Indian origin

Pie chart

ABCD

A

B

C

D

50%

28%

15%

7%

Line diagram

Sun Mon Tues Wednes Thurs Fri Satur0

10

20

30

40

50

60

70

80

Daily expenditure (RM) of a week

Example of a chart

Month 2011

Travel abroad

Exam Plantation

Conference

In Kl In home

JAN xFEB X XMAR X XAPR X XMAY XJUNE X XJULY XAUG X XSEP x XOCT XNOV X XDEC x X

Exercises

1. For each of the following random variable determine whether the variable is categorical or numerical. If numerical, determine whether the variable of interest is discrete or continuous.

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.