2. Introduction
Statistics is a field of study concerned with collection, organisation,
summarisation and analysis of data and the drawing of inferences about a body
when only a part of data are observed.
In other words, it is the scientific methodology of decision making from
collected data or information.
3. Types of Statistics
DESCRIPTIVE STATISTICS
Organisation and summarisation of data, i.e. numerical or graphics summaries
of data.
Example- Charts, graphs, tables, summary etc.
INFERENTIAL STATISTICS
Making inferences about samples drawn from population
Allows conclusion to be drawn about the data set and predictions that can be
made about relationships found between different variables.
Example- Chi square test, T-test, Anova test
4. Variables
Simple variable
• has only one main component
• example- weight, height
Composite variable
• has more than one component
• example- body mass index
Dependent variable
• one variable depends upon or is a consequence of other variable.
• example- health status of country
Independent variable
• variable that is antecedent to or the cause of the dependent variable
• example- age, gender
5. Variables
Latent variable
• the variable which cannot be measured directly but assumed to be related to a no. of
observations
• example- bright student, efficient worker.
Random variable
• when value cannot be predicted in advance
• example- tossing a coin
Attribute-
• qualitative character of an event is referred to as attribute.
• example- sex, religion,
6. Data
Data are facts expressed in numerical terms
Information
• When data set undergoes through statistical processing, it becomes
information.
Intelligence
• It is for the decision or policy makers based on transformation of the
information.
7. Classification of data
Data can be classified in various ways.
Continuous & Discrete data
Continuous is data for which an unlimited no. of possible values exist. Example is height and
weight
Discrete data is data for which limited no. of variable exist. Example- no. of players in a cricket
team.
Qualitative & quantitative data
Qualitative data- which can’t be measured, but can be expressed in frequency, example- sex,
religion.
Quantitative data- characteristic and the frequency of a variable can be measured, example- height,
weight.
Primary & secondary data
Primary data- collected by researcher themselves, example- census data, field survey
Secondary data- which has been collected by someone else and are used by another researcher.
8. Hard & soft data
Hard data- usually displayed on continuous scale as a digital readout or
computer print out, taken from modern mechanical instruments.
Soft data- any subjective measurement which has more potential for bias or
variability on the part of the observer. Example- pain of a cancer patient.
10. Tabular presentation
Different parts of table
• table number
• title (heading)
• caption (individual column/heads/boxhead)
• stubhead
• stub
• body (data field)
• footnote
• sources
11. Types of table
Simple table- describes only one set of characteristics.
Complex table- describes more than one set of characteristics.
Class interval- If data are quantitative, then one has to divide the entire data
set into number of groups or classes, which is known as ‘class interval’ .
Class limits- Each group of class interval will have an upper and lower limit.
Class magnitude (or class width)- Difference between upper and lower class
limits.
Frequency- no. of items which comes under a given class.
12. Graphic representation
Diagrams for qualitative/categorial/discrete data
Bar diagram:
• Different categories are indicated on one axis and frequency of data in each category is
indicated on the other axis and categories are compared by length of bars.
• Three different types of bar diagram- simple, multiple and component bar diagram.
Pie diagram
• It is used to represent division of whole into segments by using wedge-shaped portions of a
circle for comparison.
• Degrees of angle denote the frequency and area of sector.
Pictogram
• Small pictures or symbols are used to represent the diagram that conveys some statistical
information.
13. Map diagram
• This consists of a map of an area with location of each case of an event.
Venn diagram
• This shows the degree of overlap and exclusively for two or more
characteristics or factors within a sample, or population, or for a characteristic
among two or more samples.
32. Nominal scale
It provides a convenient way of keeping track of people, objects and
events.
Data are divided into qualitative categories or groups
Example- Hindu/Muslim/ Christian, Blood group A/B/AB/O
33. Ordinal scale
This scale places events in a meaningful order i.e. observations are
ordered or ranked on the basis of specific characteristics.
Example- Acute respiratory infection may be classified as no
pneumonia, pneumonia, severe pneumonia
34. Interval scale
Similar to ordinal scale, here data are placed in meaningful order and
in addition they have definite interval between them.
Example- In Celsius scale, difference between 100 degree and degree.
35. Ratio scale
This scale has some properties as an interval scale; nut because it has
an absolute zero, meaningful ratios do exist.
Example- weight in grams or pounds, time in seconds or days.
36. THANK YOU
“sukhaduḥkhe same kṛtvā lābhālābhau jayājayau
tato yuddhāya yujyasva naivaṃ pāpamavāpsyasi”
“Holding pleasure and pain, gain and loss, victory and defeat as
alike, gird yourself up for the battle.
Thus, you shall not incur any sin.”