course code: bft2074 course title. introduction and data collection 1.1 some definitions...
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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 diff ers for diff erent subjects. For example, if
the weight of 30 subjects were measured, then weight would be a
variable. If no. of students in diff erent 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 sample is less time consuming
2. A sample is less costly to administer than a census
Note: A sample must be representative for specific population/subpopulation
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
27
109
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
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 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
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.
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