The document discusses various methods of collecting and presenting data. It describes primary and secondary data collection methods. Primary data is originally collected for a study, while secondary data has already been collected by others. Methods to collect primary data include direct investigation, questionnaires, and schedules. Secondary data can come from published reports. The document also discusses categorical and numerical data types and how to present each type. Categorical data can be presented in summary tables, bar charts, and pie charts. Numerical data presentation methods include frequency distributions, histograms, frequency polygons, and ogives.

1. Presented by:
Nasif Hassan Khan Abir ………… ID # 61531-24-007
Md. Ferdaus Alam ………… ID # 61531-24-010
Zakir Husain ………… ID # 61325-18-058
Md. Faruqul Islam ............ ID # 61325-18-029

2. Data Collection
The collection, organization, and presentation of data are basic background
material for learning descriptive and inferential statistics and their applications
Method of Collecting Data
On the basis of the source of collection data may be classified as:
 Primary data
 Secondary data
Types of Data
There are two types of data.They are:
 Numerical Data
 Categorical Data

3. Collection of Data
The data which are originally collected for the first time for the purpose of the
survey are called primary data. For example facts or data collected regarding the
habit of taking tea or coffee in a village by an investigator.
Method of Collecting Primary Data
There are several methods for collecting primary data. Some of them are:
 Direct personal investigation
 Indirect investigations
 Through correspondent
 By mailed questionnaire
 Through schedules

4. Secondary Data
When we use the data, which have already been collected by others, the data are
called secondary data.This data is said to be primary for the agency which
collects it first, and it becomes secondary for all the other users.
Method of Collecting Secondary Data
 Published reports of newspapers, RBI and periodicals.
 Publication from trade associations
 Financial data reported in annual reports
 Information from official publications
 Publication of international bodies such as UNO,World Bank etc.
 Internal reports of the government departments
 Records maintained by the institutions
 Research reports prepared by students in the universities

5. Categorical Data
Categorical data is the statistical data type consisting of categorical variables or
of data that has been converted into that form, for example as grouped data. For
example- Marital Status, Political Party, Eye Color, etc.
NumericalData
Numerical values or observations can be measured. And these numbers can be
placed in ascending or descending order. Numerical data can be divided into two
groups:
 Discrete(Counted Items such as- number of children, defects per hour etc.)
 Continuous(Measured Characteristics such as- weight, voltage etc.)

6. Interval Data
Ordinal Data
Nominal Data
Height,Age,Weekly Food
Spending
Service quality rating,
Standard & Poor’s bond
rating, Student letter
grades
Marital status,Type of car
owned
Ratio Data
Temperature in Fahrenheit,
Standardized exam score
Categories (no ordering or
direction)
Ordered Categories
(rankings, order, or scaling)
Differences between
measurements but no true
zero
Differences between
measurements, true zero
exists
EXAMPLES:

7. Presentation of Data
Data collected in the form of schedules and questionnaires are not self
explanatory. These are in the form of raw data. In order to make them
meaningful, these are to be made presentable.
Presentation of Categorical Data
Categorical Data can be presented by two ways:
 Tabulating Data(SummaryTable)
 Graphing Data (Bar Chart, Pie Chart, Pareto Diagram)

8. The summary table is a visualization that summarizes statistical information
about data in table form.
Example: Current Investment Portfolio
Investment Amount Percentage
Type (in thousands $) (%)
Stocks 46.5 42.27
Bonds 32.0 29.09
CD 15.5 14.09
Savings 16.0 14.55
Total110.0 100.0

9. Bar charts are often used for qualitative data (categories or nominal scale). Height
of bar shows the frequency or percentage for each category. Bar Chart for the
previous summary table is
0 10 20 30 40 50
Stocks
Bonds
CD
Savings
Amount in $1000's
Investor's Portfolio

10. Pie charts are often used for qualitative data (categories or nominal scale). Size of
pie slice shows the frequency or percentage for each category. Pie Chart for the
previous summary table is shown below

11.  Used to portray categorical data
 A bar chart, where categories are shown in descending order of frequency
 A cumulative polygon is often shown in the same graph
 Used to separate the “vital few” from the “trivial many”
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Stocks Bonds Savings CD
cumulative % invested
(line graph)
% invested in each
category (bar
graph)
Current Investment Portfolio
Series1
Series2

12. Categorical Data can be presented by two ways:
 Ordered Array (Stem-and-Leaf Display)
 Frequency/Cumulative Distributions (Histogram, Polygon, Ogive)
Ordered Array
 A sequence of data in rank order:
 Shows range (min to max)
 Provides some signals about variability within the range
 May help identify outliers (unusual observations)
 If the data set is large, the ordered array is less useful
 Example- Data in raw form (as collected): 24, 26, 24, 21, 27, 27, 30, 41, 32, 38
 Data in ordered array from smallest to largest:21, 24, 24, 26, 27, 27, 30, 32, 38, 41

13. A simple way to see distribution details in a data set.To make this diagram first
We have to separate the sorted data series into leading digits (the stem) and the
trailing digits (the leaves).
Stem and Leaves of 21, 38 and 41 is,
Stem Leaf
2 1
3 8
4 1

14. What is a Frequency Distribution?
 A frequency distribution is a list or a table
 Containing class groupings (ranges within which the data fall)
 The corresponding frequencies with which data fall within each grouping or
category.
The reasons for using Frequency Distributions are:
 It is a way to summarize numerical data
 It condenses the raw data into a more useful form
 It allows for a quick visual interpretation of the data

15. Class Intervals and Class Boundaries
 Each class grouping has the same width
 Determine the width of each interval by
 Usually at least 5 but no more than 15 groupings
 Class boundaries never overlap
 Round up the interval width to get desirable endpoints
groupingsclassdesiredofnumber
range
intervalofWidth 

16. A manufacturer of insulation randomly selects 20 winter days and records the
daily high temperature
24, 35, 17, 21, 24, 37, 26, 46, 58, 30, 32, 13, 12, 38, 41, 43, 44, 27, 53, 27
For frequency distribution we need to follow the following steps:
 Sort raw data in ascending order:
12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58
 Find range: 58 - 12 = 46
 Select number of classes: 5 (usually between 5 and 15)
 Compute class interval (width): 10 (46/5 then round up)
 Determine class boundaries (limits): 10, 20, 30, 40, 50, 60
 Compute class midpoints: 15, 25, 35, 45, 55
 Count observations & assign to classes

18.  A graph of the data in a frequency distribution is called a histogram
 The class boundaries (or class midpoints) are shown on the horizontal axis
 the vertical axis is either frequency, relative frequency, or percentage
 Bars of the appropriate heights are used to represent the number of observations
within each class
Example-For previous data the Histogram should be like this.There will be no gap
between bars.
0
1
2
3
4
5
6
7
5 15 25 35 45 55 65
Frequency
Class Midpoints
Histogram: Daily High Temperature

19. In a percentage polygon the vertical axis would be defined to show the
percentage of observations per class.
Example-For previous data the Frequency Polygon should be like this,
0
1
2
3
4
5
6
7
5 15 25 35 45 55 65
Frequency
Class Midpoins
Frequency Polygon: Daily High Temperature

20. It is also known as the cumulative percent polygon.
Example-For previous data the Ogive or Cumulative percent Polygon should be
like this,
0
10
20
30
40
50
60
70
80
90
100
10 20 30 40 50 60
CumulativePercentage
Class Boundaries (Not Midpoints)
Ogive: Daily High Temperature

21.  Not distorting the data
 Avoiding unnecessary adornments (no “chart junk”)
 Using a scale for each axis on a two-dimensional graph
 The vertical axis scale should begin at zero
 Properly labeling all axes
 The graph should contain a title
 Using the simplest graph for a given set of data