Bureaucrats, oversight, Administrators
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Line 219: − + − + + + + + + + + + − + − + + + + + − + − + − + − + − + − + − − + − + + + + + + + + + + + + − + − Secondary data is+ − primary data that has been summarised or processed, for example, the+ − set+ − + + + + + + − + − gave as favourite colours would be secondary data because it is a+ + + + + + + − summary of responses.+ − Data already collected prior our use is secondary data. Primary data+ − is what we collect as a part of our study. All processed data+ − therefore is also secondary.+ − + + + + + + + − Transforming primary+ − data into secondary data through analysis, grouping or organisation − into secondary data is the process of generating information. + + + + + − === Purpose of Collecting Primary Data ===+ + + − Data is collected to+ − provide answers that help with understanding a particular situation.+ − Here are examples to illustrate some real world data collections+ − scenarios in the categories of qualitative and quantitative data.+ + + + + − === Qualitative Data ===+ − + − * The local government might want to know how many residents have electricity and might ask the question: ”Does your home have a safe supply of electricity?”+ − * A supermarket manager might ask the question: “What flavours of soft drink should be stocked in my supermarket?” The question asked of customers might be “What is your favourite soft drink?” Based on the customers’ responses, the manager can make an informed decision as to what soft drinks to stock. − * A company manufacturing medicines might ask “How effective is our pill at relieving a headache?” The question asked of people using the pill for a headache might be: “Does taking the pill relieve your headache?” Based on responses, the company learns how effective their product is. − * A motor car company might want to improve their customer service, and might ask their customers: “How can we improve our customer service?” − * A teacher may ask “How many hours of TV by students on TV' to get an idea of what children are learning from TV at home and how it supplements (or affects) the learning in the school + + + + + − === Quantitative Data ===+ + + − * A cell phone manufacturing company might collect data about how often people buy new cell phones and what factors affect their choice, so that the cell phone company can focus on those features that would make their product more attractive to buyers.+ − * A town councillor might want to know how many accidents have occurred at a particular intersection, to decide whether a robot should be installed. The councillor would visit the local police station to research their records to collect the appropriate data.+ − * A supermarket manager might ask the question: “What flavours of soft drink should be stocked in my supermarket?” The question asked of customers might be “What is your favourite soft drink?” Based on the customers’ responses, the manager can make an informed decision as to what soft drinks to stock.+ − * What kind of TV programs are watched by students, how many are educational in nature. − However, it is important to note that+ − different questions reveal different features of a situation, and+ − that this affects the ability to understand the situation. For+ − example, if the question in the list What kind of TV programs are − watched by students, how many are educational in nature. was − re-phrased to be: Do your children watch educational programs on TV − and if you answered yes, but most programs being watched were of − entertainment value, , then this could give the wrong impression that − TV was being used as an educational tool in your home . − == Data Collection ==+ + + − The method of+ − collecting the data must be appropriate to the question being asked.+ − Some − examples of data+ − collecting methods are:+ − − # Experiments − # Questionnaires, surveys, focus group discussions and interviews − # Other sources (friends, family, newspapers, books, magazines and now increasingly the Internet) − # Observation − # Specialised equipment (rainwater gauges to measure rainfall in a place, various medical equipment that collect information about different biological processes) + + + + + − The most important+ − aspect of each method of data collecting is to clearly formulate the+ − question that is to be answered. The details of the data collection − should therefore be structured to take your question into account. − + + + − You must have observed+ − your teacher recording the attendance of students in your class+ − everyday, or recording marks obtained by you after every test or − examination. Similarly, you must have also seen a cricket score − board. One score boards have been illustrated here : + + + + + − NatWest One Day+ − International Series: England v India+ − Friday, 16 September 2011 at − The Swalec Stadium − '''England beat India − by 6 wickets (D/L). '''England won the toss and decided to field − − − {| border="1" − |- − [[India Innings]]+ − 304 for 6 (50.0 overs)+ + − [[England Innings]]+ − 241 for 4 (32.2 overs)+ + − + − + − '''India − 1st Innings - Close''' − − − {| border="1" − |- − + − + − Runs+ − Balls+ + − 4s+ − 6s+ − |- − P Patel+ − c Bresnan+ − b Swann+ − '''19'''+ − 39+ + − 0+ − 0+ − |- − Rahane+ − c Finn+ − b Dernbach+ − '''26'''+ − 47+ + − 3+ − 0+ − |- − Dravid+ − + − b Swann+ − '''69'''+ − 79+ + − 4+ − 0+ − |- − Kohli+ − hit wicket+ − b Swann+ − '''107'''+ − 93+ + − 9+ − 1+ − |- − Raina − − | − c Bresnan − b Finn+ − '''15'''+ − 15+ Line 549:
Line 578: + − 1+ − |- − Dhoni+ − not out+ − + − '''50'''+ + − 26+ − 5 − − | − 2 − |- − Jadeja+ − c Bopara+ − b Dernbach+ − + − '''0''' − + − + − + + + + + + + − 0+ − 0+ − |- − Ashwin+ − not out+ + − + − '''0'''+ Line 633:
Line 663: − 0+ Line 642:
Line 672: − '''Extras'''+ − + − 6w 1b 11lb+ − '''18'''+ − + − '''Total'''+ + + + + − for 6+ − '''304'''+ + − '''(50.0 ovs)'''+ − + + − + − − {| border="1" − |- − | − {| border="1" − |- − Bowler+ − Overs+ − Maidens+ + − Runs+ − Wickets+ − |- − Bresnan+ − 9.0+ Line 724:
Line 754: + − 62+ − + − |- − Finn+ − 10.0+ − 1+ − + − 44 − − | − 1 − + − | − Dernbach − − | + − 10.0 − − − | − 0 − + + + + + + + + + − 73+ − | + − 2 − |-+ − | − Swann − | + − 9.0 − | + − 0 − | + − 34 − | + − 3 − |-+ − | − S Patel − | + − 8.0 − | + − 0 − 55+ − | + − 0 − |-+ − | − Bopara − | + − 4.0 − | + − 0 − | + − 24 − | + − 0 − |} + − − |} + − − == Recording Data ==+ − − Let us take an example of a class which is − preparing to go for a picnic. The teacher asked the students to give − their choice of fruits out of banana, apple, orange or guava. Uma is − asked to prepare the list. She prepared a list of all the children − and wrote the choice of fruit against each name. This list would help − the teacher to distribute fruits according to the choice. + − + − + − + + + + + − Raghav — Banana+ − Preeti — Apple+ + − Amar — Guava+ + + − Fatima — Orange+ − Amita — Apple+ − Raman — Banana+ + − Radha — Orange+ − Farida — Guava+ − Anuradha — Banana+ + + + + − + − Rati — Banana+ + + + − Bhawana — Apple+ − Manoj — Banana+ + − Donald — Apple+ + + − Maria — Banana+ + + + − Uma — Orange+ + − Akhtar — Guava+ − Ritu — Apple+ − Salma — Banana+ + − Kavita — Guava+ − Javed — Banana+ Line 926:
Line 949: + + + + − + − − − Example 1 : A teacher − wants to know the choice of food of each student as part of the − mid-day meal programme. The teacher assigns the task of collecting − this information to Maria. Maria does so using a paper and a pencil. − After arranging the choices in a column, she puts against a choice of − food one ( | ) mark for every student making that choice. − − + + + + Line 962:
Line 983: − + + − + + − + − + + + + + + − Umesh, after seeing the+ − table suggested a better method to count the students. He asked − Maria to organise the marks ( | ) in a group of ten as shown below : − + + + + + + + − Choice+ − + − + − Rice only+ − Chapati only+ + − Both rice and chapati+ + + − |||||||||| |||||||+ − + + − + + + − 17+ − 13+ + − 20+ + − + + + + + + − Rajan made it simpler+ − by asking her to make groups of five instead of ten, as+ − shown below :+ − − − {| border="1" − |- − | − Choice + + + + + + + + − | + − Tally marks − − | + − Number of students+ − + + + + + + + + − Rice only+ − Chapati only+ + − Both rice and chapati+ + + − ||||| ||||| + − ||||| || − ||||| ||||| + − |||+ − + + + − 17+ − 13+ + − 20+ + − + + + + + + + + + + + + − === Meaning of Frequency ===+ − + − Frequency means the number of occurrences within a − given time period. It is not easy to answer the − question looking at the choices written haphazardly. We arrange the − data in Table below using tally marks. − − − − {| border="1" − |- − Subject+ − + − Tally Marks+ − + − | + − Number of Students − − |-+ − | + − Art+ − | + − |||| ||+ + − | + − 7+ + + + + + + + + + + − Mathematics+ − + + + + + + + + + − 5+ − |- − Science+ − |||||+ − 6+ − English+ − ||||+ − 4+ − + − The number of tallies+ − before each subject gives the number of students who like that − particular subject. This is known as the frequency of that subject. − Frequency gives the number of times that a particular entry occurs. − From above table, Frequency of students who like English is 4 − Frequency of students who like Mathematics is 5 The table made is − known as frequency distribution table as it gives the number of times − an entry occurs. − − === Categorical Frequency Distributions === − − Categorical frequency − distributions - can be used for data that can be placed in specific − categories, such as nominal- or ordinal-level data. (nominal or − ordinal also called discrete data is where we can distinctly count − the occurrences of a variable). + − Examples - political+ − affiliation, religious affiliation, blood type etc. Below is Blood+ − Type frequency distribution example. + − + − {| border="1" − Class+ − Frequency+ − Percent+ − |- − A+ − 5+ − 20+ − |- − B+ − 7 − − | − 28 − C+ − 9+ − 36+ − |- − D+ − 4+ − 16+ − + − − == Activities == − === Activity 1 Data Collection ===+ + + − ==== Learning Objectives ====+ + + + − Understand collection of data .+ + − ==== Materials and resources required ==== − − Paper & Pen − − − ==== Pre-requisites/ Instructions ==== − − The meaning of data and how to data is organised − in a tabular form − − − ==== Method ==== − − The table below has spaces for up to 10 entries. − The first four columns have headings. Choose headings for the other − columns and collect data from the 10 of your class mates − − − {| border="1" − |- − '''Name''' − − | − '''Age''' − '''Height''' − − | − '''Favourite Colour ''' Line 1,531:
Line 1,561: − + − | − + − | + − + + + + − | + − − − | + − + − | + − + − + − − | − − − − | − − − − | − − − − |- − | − − − − | − − − − | − − − − | − − − − | − − − − | − − − − | − − − − | − − − − |- − | − − − − | − − − − | − − − − | − − − − | − − − − | − − − − | − − − − | − − − − |- − | − − − − | − − − − | − − − − | − − − − | − − − − | − − − − | − − − − | − − − − |} − − − − − ==== Evaluation ==== − − Looking at the table − and data can the student answer the following questions ? − − − # Does any student like green the most ? − # Do you think red is the most popular colour, why ? − # What other information did you come to know about each student ? − − == Evaluation == − − At the end of this sub-topic the student should be − able to − − − − − Line 1,694:
Line 1,588: − +
→Data Handling
include identifying data, collecting data, organising/representing
include identifying data, collecting data, organising/representing
data and summarising data.
data and summarising data.
== Objective ==
== Objective ==
people on their height or weight.
people on their height or weight.
Secondary data is
primary data that has been summarised or processed, for example, the
set of colours that people gave as favourite colours would be secondary data because it is a
summary of responses.
Data already collected prior our use is secondary data. Primary data
is what we collect as a part of our study. All processed data
therefore is also secondary.
Transforming primary
data into secondary data through analysis, grouping or organisation
into secondary data is the process of generating information.
=== Purpose of Collecting Primary Data ===
Data is collected to
provide answers that help with understanding a particular situation.
Here are examples to illustrate some real world data collections
scenarios in the categories of qualitative and quantitative data.
=== Qualitative Data ===
* The local government might want to know how many residents have electricity and might ask the question: ”Does your home have a safe supply of electricity?”
* A supermarket manager might ask the question: “What flavours of soft drink should be stocked in my supermarket?” The question asked of customers might be “What is your favourite soft drink?” Based on the customers’ responses, the manager can make an informed decision as to what soft drinks to stock.
* A company manufacturing medicines might ask “How effective is our pill at relieving a headache?” The question asked of people using the pill for a headache might be: “Does taking the pill relieve your headache?” Based on responses, the company learns how effective their product is.
* A motor car company might want to improve their customer service, and might ask their customers: “How can we improve our customer service?”
* A teacher may ask “How many hours of TV by students on TV' to get an idea of what children are learning from TV at home and how it supplements (or affects) the learning in the school
=== Quantitative Data ===
* A cell phone manufacturing company might collect data about how often people buy new cell phones and what factors affect their choice, so that the cell phone company can focus on those features that would make their product more attractive to buyers.
* A town councillor might want to know how many accidents have occurred at a particular intersection, to decide whether a robot should be installed. The councillor would visit the local police station to research their records to collect the appropriate data.
* A supermarket manager might ask the question: “What flavours of soft drink should be stocked in my supermarket?” The question asked of customers might be “What is your favourite soft drink?” Based on the customers’ responses, the manager can make an informed decision as to what soft drinks to stock.
* What kind of TV programs are watched by students, how many are educational in nature.
However, it is important to note that
different questions reveal different features of a situation, and
that this affects the ability to understand the situation. For
example, if the question in the list What kind of TV programs are
watched by students, how many are educational in nature. was
re-phrased to be: Do your children watch educational programs on TV
and if you answered yes, but most programs being watched were of
entertainment value, , then this could give the wrong impression that
TV was being used as an educational tool in your home .
== Data Collection ==
The method of
collecting the data must be appropriate to the question being asked.
Some examples of data collecting methods are:
# Experiments
# Questionnaires, surveys, focus group discussions and interviews
# Other sources (friends, family, newspapers, books, magazines and now increasingly the Internet)
# Observation
# Specialised equipment (rainwater gauges to measure rainfall in a place, various medical equipment that collect information about different biological processes)
of colours that people
The most important
aspect of each method of data collecting is to clearly formulate the
question that is to be answered. The details of the data collection
should therefore be structured to take your question into account.
You must have observed your teacher recording the attendance of students in your class
everyday, or recording marks obtained by you after every test or
examination. Similarly, you must have also seen a cricket score
board. One score boards have been illustrated here :
NatWest One Day
International Series: England v India
Friday, 16 September 2011 at
The Swalec Stadium
'''England beat India
by 6 wickets (D/L). '''England won the toss and decided to field
{| border="1"
|-
|
[[India Innings]]
304 for 6 (50.0 overs)
|-
|
[[England Innings]]
241 for 4 (32.2 overs)
|}
'''India
1st Innings - Close'''
{| border="1"
|-
|
|
|
Runs
|
Balls
|
4s
|
6s
|-
|
P Patel
|
c Bresnan
|
b Swann
|
'''19'''
|
39
|
0
|
0
|-
|
Rahane
|
c Finn
|
b Dernbach
|
'''26'''
|
47
|
|
3
|
0
|-
|-
|
|
Dravid
|
|}
|
b Swann
|
|
'''69'''
|
|
79
|
|
4
|
|
0
|-
|
|
Kohli
|
|
hit wicket
|
|
b Swann
|
|
'''107'''
|
|
93
|
|
9
|
|
1
|-
|
|
Raina
|
|
c Bresnan
|
|
b Finn
|
|
'''15'''
|
|
15
|
|
0
|
|
1
|-
|
|
Dhoni
|
|
not out
|
|
|
|
'''50'''
|
|
26
|
|
5
|
|
2
|-
|
|
Jadeja
|
|
c Bopara
|
|
b Dernbach
|
|
'''0'''
|
|
1
|
|
0
|
|
0
|-
|
|
Ashwin
|
|
not out
|
|
|
|
'''0'''
|
|
0
|
|
0
|-
|
|
'''Extras'''
|
|
|
|
6w 1b 11lb
|
|
'''18'''
|
|
|-
|
|
'''Total'''
|
|
|
|
for 6
|
|
'''304'''
|
|
'''(50.0 ovs)'''
|
|}
|
1
{| border="1"
|-
|
{| border="1"
|-
|
Bowler
|
|
Overs
|
|
Maidens
|
|
Runs
|
|
Wickets
|-
|
|
Bresnan
|
|
9.0
|
|
62
|-
|-
|
|
Finn
|
|
10.0
|
|
1
|
|
44
|
|
1
|-
|-
|
|
Dernbach
|
|
10.0
|
0
|
|
73
|
|
2
|-
|
|
Swann
|}
|
9.0
|
|
0
|
|
34
|
|
3
|-
|
|
S Patel
|
|
8.0
|
|
0
|
|
55
|-
|
|
Bopara
|
|
0
4.0
|
|
0
|
|
24
|
|
0
|
|}
|-
|}
== Recording Data ==
Let us take an example of a class which is
preparing to go for a picnic. The teacher asked the students to give
their choice of fruits out of banana, apple, orange or guava. Uma is
asked to prepare the list. She prepared a list of all the children
and wrote the choice of fruit against each name. This list would help
the teacher to distribute fruits according to the choice.
{| border="1"
|-
|
|
Raghav — Banana
Preeti — Apple
Amar — Guava
Fatima — Orange
Amita — Apple
Raman — Banana
Radha — Orange
Farida — Guava
Anuradha — Banana
Rati — Banana
|
|
Bhawana — Apple
Manoj — Banana
Donald — Apple
Maria — Banana
Uma — Orange
Akhtar — Guava
Ritu — Apple
Salma — Banana
Kavita — Guava
Javed — Banana
|}
Example 1 : A teacher
wants to know the choice of food of each student as part of the
mid-day meal programme. The teacher assigns the task of collecting
this information to Maria. Maria does so using a paper and a pencil.
After arranging the choices in a column, she puts against a choice of
food one ( | ) mark for every student making that choice.
{| border="1"
{| border="1"
|-
|-
|
|
Choice
|
Number of students
|-
|
Rice only
Chapati only
Both rice and chapati
|
|||||||||||||||||
|||||||||||||
||||||||||||||||||||
|}
Umesh, after seeing the
table suggested a better method to count the students. He asked
Maria to organise the marks ( | ) in a group of ten as shown below :
{| border="1"
|-
|
Choice
|
|
Tally marks
|
Number of students
|-
|
Rice only
Chapati only
Both rice and chapati
|
|||||||||| |||||||
|||||||||| |||
|||||||||| ||||||||||
|
17
13
20
Rajan made it simpler
by asking her to make groups of five instead of ten, as
shown below :
{| border="1"
{| border="1"
|-
|-
|
|
Choice
Choice
|
Tally marks
|
|
|||||||||||||||||
||||| |||||
||||| ||
|||||||||||||
||||| |||||
|||
||||||||||||||||||||
||||| ||||| ||||| |||||
|}
|
17
13
20
|}
=== Meaning of Frequency ===
Frequency means the number of occurrences within a
given time period. It is not easy to answer the
question looking at the choices written haphazardly. We arrange the
data in Table below using tally marks.
{| border="1"
{| border="1"
|-
|-
|
|
Subject
|
|
Tally marks
Tally Marks
|
|
Number of students
Number of Students
|-
|-
|
|
Art
|
|||| ||
|
7
|-
|
|
Mathematics
|||||||||| |||
|
||||
|||||||||| ||||||||||
|
5
|-
|
|
Science
|
|||||
|
6
|}
|-
|
English
|
||||
|
4
|}
The number of tallies
before each subject gives the number of students who like that
particular subject. This is known as the frequency of that subject.
Frequency gives the number of times that a particular entry occurs.
From above table, Frequency of students who like English is 4
Frequency of students who like Mathematics is 5 The table made is
known as frequency distribution table as it gives the number of times
an entry occurs.
=== Categorical Frequency Distributions ===
Categorical frequency
distributions - can be used for data that can be placed in specific
categories, such as nominal- or ordinal-level data. (nominal or
ordinal also called discrete data is where we can distinctly count
the occurrences of a variable).
Examples - political
affiliation, religious affiliation, blood type etc. Below is Blood
Type frequency distribution example.
{| border="1"
|-
|-
|
|
Class
|
Frequency
|
Percent
|-
|
|
A
|
5
||||| ||||| ||||| |||||
|
20
|-
|
|
B
|
7
|
28
|}
|-
|
C
|
9
|
36
|-
|
D
|
4
|
|
16
|
|}
== Activities ==
=== Activity 1 Data Collection ===
==== Learning Objectives ====
Understand collection of data .
==== Materials and resources required ====
Paper & Pen
==== Pre-requisites/ Instructions ====
The meaning of data and how to data is organised
in a tabular form
==== Method ====
The table below has spaces for up to 10 entries.
The first four columns have headings. Choose headings for the other
columns and collect data from the 10 of your class mates
{| border="1"
|-
|-
|
|
'''Name'''
|
|
||||
'''Age'''
|
'''Height'''
|
'''Favourite Colour '''
|
|
|
|
|
|
|
|
|-
|-
|
|
|
|
|
|
|}
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|}
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|-
|}
==== Evaluation ====
Looking at the table
and data can the student answer the following questions ?
# Does any student like green the most ?
# Do you think red is the most popular colour, why ?
# What other information did you come to know about each student ?
== Evaluation ==
At the end of this sub-topic the student should be
able to
# Identify the different types of data
# Collect, classify and organise data in a tabular form
# Calculate the frequency of data
# Identify the different types of data
# Collect, classify and organise data in a tabular form
# Calculate the frequency of data
# Interpret data that is given in a tabular form
# Interpret data that is given in a tabular form
== Enrichment Activities ==
== Enrichment Activities ==
= Graphical representation of Data =
= Graphical representation of Data =