Lect w1 observed_data_and_their_characteristics

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Lect 1. Course code: FFT2074 Course Title

BIOMETRY AND EXPERIMENTAL

DESIGN!

Observed data & their Characteristics!

Prof Dr Md Ruhul Amin

Introduction and Data Collection1.1 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.

….DefinitionsDescriptive 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 statisticsInferential stat ist ics is the method o f es t imat ing 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 is better than that onee.g. Mean height of SBH male students: 5’

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.

Biometry● Statistics applied in the field of Life Science

is called !

BIOMETRY or!

BIOSTATISTICS!

Life 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 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)

Observation of a particular eventGenerally 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 variable1. 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 typesThere 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.

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 scalingCategorical variables Categories

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

Marital status Single / Married /Widow

3. Example of ordinal scalingCategorical 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

Classification of variableVariable

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

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

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

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

Example 2. Frequency dataThe following data were collected by randomly sampling a large population of rainbow trout. The variability of interest is weight (lb)

Xi f i fi

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

fXf

X∑∑

= 037.427109

==

A sample of bar graph/histogram

Series 1Series 2Series 3

Distribution of 3 (series) races in 4 states (category) of Malaysia

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

Pie chart

Line Diagram

Example of a chartMonth 2011

Travel abroad

Exam Plantation

Conference

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

Exercises1. 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.

Exercise 1No. of telephones per household

Type of telephone primarily usedNo. of long-distance call made per month

Length (minute) of long-distance call made per month

Colour of telephone primarily usedMonthly charge (RM) for long-distance call made

No. of local call made per monthWhether there is a telephone line connected to a computer modem in the household

Colour

Thank you

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