2. INTRODUCTION
• The word statistics has two meanings. That is we can
define Statistics in two senses.
Statistics defined in plural sense (As a data)
• In this sense, it is equivalent to referring to numerical
facts, figures or statistical data. i.e., the raw data
themselves, like statistics of births, statistics of
students, statistics of imports and exports, etc.
• Statistics defined as a method (Singular Sense)
• The second meaning of statistics refers to the science
or discipline of study.
• In this sense of the word, Statistics is defined as the
science of collecting, presenting, analyzing and
interpreting numerical data to make decisions.
3. Characteristics of statistics
• not all numerical data are statistics.
• Some of the characteristics which numerical data
must possess in order that they may be called
statistics are given below.
– Statistics should be numerically expressed
– They should be aggregates of facts
– They should be collected in a systematic manner
– They should be collected for a predetermined purpose
– They should be placed in relation to each other
– They should be enumerated or estimated according to
reasonable standards of accuracy
– Statistics should be affected to a marked extent by a
multiplicity of causes
• In general it can be said that all statistics are
numerical facts; but not all numerical facts are
statistics.
4. BASIC TERMINOLOGIES IN STATISTICS
1. Data: In Statistics, all conclusions are based on facts and the first
step in any statistical investigation is to collect a set of related
observations from which conclusions may be drawn.
• Such related observations that form the set are known as Data.
• The word data was obtained from the singular Latin word “datum
“to mean fact.
2. Population: The complete collection of individuals, objects, or
measurements that have a characteristic in common or totality
of related observations in a given study is described as a
population.
• The population that is being studied is also called the target
population.
• Population can be finite (limited in its size) or infinite
(unrestricted).
3. Sample: A sub group of the population that will be studied in
detail is called a Sample.
5. 4. Parameters: are statistical measures obtained from a population
data.
These measures may include the mean, variance, standard
deviation, etc. and are denoted by , etc. respectively.
4. Sample Statistic: a number computed from a sample data.
Sample Statistics are denoted by lower case letters of the
alphabet such as -the sample mean, - the sample variance, etc
5. Variable: is a characteristic under study that assumes different
values for different elements and most of the time variables are
denoted by the letters X, Y, Z, etc. Example 1.3: Height, Weight,
Age, Income, Expenditure, Grade, Intelligence, sex, color, etc.
6. Quantitative Variable: is variable that can be expressed
numerically such as height, weight, age, income,
expenditure, grade, family size, number of students in a class,
etc.
7. Qualitative Variable: is variable that cannot assume a numerical
value but can be classified into two or more nonnumeric
categories such as the gender of a person, the language in which
a book is written, hair color, and so on.
6. • Discrete Variable: is a variable whose values are
countable such as family size, number of students in a
class, etc. Its values are obtained by counting.
• Continuous Variable: is a variable which can,
theoretically assume any numerical value between two
given values.
• Observation or Measurement: The value of a variable
for an element is called an Observation or
Measurement.
• Data Set: A data set is a collection of observations on
one or more variables.
• Sample Frame: A list of the entire population from
which items can be selected to form a sample is
referred to as sample frame.
7. 1.4. CLASSIFICATION OF STATISTICS
1. Descriptive Statistics consists of the collection,
organization, presentation and analysis of numerical data.
• It is concerned with describing certain characteristics of
a set of data (usually a sample) – that is, what it is
shaped like, what number the values tend to cluster
(converge) around, how much variation is present in
the data, and so forth.
• In short, Descriptive Statistics describes the nature or
characteristics of data without making conclusion or
generalization.
• Example 1.4: The average age of athletes participated
in London Marathon was 25 years, 80% of the
employees of the company are males, The marks of 50
students in Statistics course are found to range from 30
to 85, etc. are some examples of Descriptive Statistics.
8. 2. Inferential Statistics
• Inferential Statistics is concerned with the process
of drawing conclusions (inferences) about specific
characteristics of a population based on information
obtained from samples, performing hypothesis
testing, determining relationships among variables,
and making predictions.
• Example 1.5: - The result obtained from the
analysis of the income of 100 randomly selected
citizens in Ethiopia suggests that the average per
capita income of a citizen in Ethiopia is 30 Birr.
• The average income of all families in Ethiopia can be
estimated from figures obtained from a few
hundred families.
9. 1.5 STEPS OF STATISTICAL INVESTIGATION
1. Collection of data
2. Organization of data
3. Presentation
4. Analysis of Data
5. Interpretation of data
10. 1. Collection of data
• This is the process of obtaining measurements or
counts and constitutes the first step in statistical
investigation.
• In general, information pertinent to the underlying
investigation is collected.
• Valid conclusions can only result from properly
collected data. i.e., if data are faulty, the
conclusions drawn can never be reliable.
• Hence, utmost care must be exercised in collecting
data because they form the foundation of statistical
analysis
11. 2. Organization of data
• Collected data have to be organized in a suitable form so that
one can have a general understanding of the information
gathered. A large mass of figures that are collected from
surveys frequently need organization
• The first step in organizing a group of data is editing. The
collected data must be edited very carefully so that
omissions, inconsistencies, irrelevant answers and wrong
computations in the returns from a survey may be corrected
or adjusted
• The next step is to classify the data. The purpose of data
classification is to arrange them according to some common
Characteristics possessed by the items constituting the data
• The last step in data organization is tabulation. The purpose
of tabulation is to arrange the data in columns and rows so
that there is absolute clarity in the data presented.
12. 3. Presentation
• The main purpose of data presentation is to facilitate
statistical analysis.
• This can be done by arranging the data using graphs
and diagrams.
4. Analysis of Data
• This is the extraction of summarized and
comprehensible numerical descriptions of the data
where these measures will in turn give a far better
understanding of the nature of the data.
• The purpose of analyzing data is to dig out information
useful for decision making
• The most commonly used methods of statistical
analysis are measures of central tendency, measures of
variation, correlation, regression, estimation and
hypothesis testing
13. 5. Interpretation of data
• Is the task of drawing conclusions from the analysis
of the data and usually involves the formulation of
predictions concerning a large collection of
objects from information available for a small
collection of similar objects.
• This step usually involves decision making about a
large collection of objects (Population) and about
information gathered from a small collection of
similar objects (sample).
• The interpretation of data is a difficult task and
necessitates a high degree of skill and experience.
14. 1.6 APPLICATIONS OF STATISTICS
• The study of statistics has become more popular than ever
during the past three decades or so.
• The increasing availability of computers and statistical soft
ware packages has enlarged the role of statistics as a tool for
empirical research.
• As a result, statistics is used for research in almost all
professions, from medicine to sports, Today college students
in almost all disciplines are required to take at least one
statistics course.
• So the various tools of statistics are being used to solve
problems in everyday life, in research, in marketing, in
planning, in production and quality control, and other areas.
• widely used in all areas of human knowledge and widely
applied in a variety of disciplines such as business, economics
and research.
15. 1.6.1 LIMITATION OF STATISTICS
i. It does not deal with individual values.
ii. It cannot deal with qualitative characteristic.
Statistics deals with quantitative characteristics
iii. Statistical conclusions are not universally true.
iv. Statistical interpretation requires a high degree of
skill and understanding of the subject.
v. Statistics can be misused. Statistics can be used to
establish wrong conclusions and, therefore, can be
used only by experts.
16. 1.6.2 USES OF STATISTICS
To present facts in a definite form
Statistics facilitates comparisons
Statistics gives guidance in the formulation of
suitable policies
Prediction
Statistical methods are very helpful in formulating
and testing hypothesis and to develop new
theories
Statistics in the sciences.
17. 1.7 SCALES OF MEASUREMENT
• Measurement can be defined as the assignment of numbers
to objects and events according to logically acceptable rules.
• The number system is highly logical and offers a multiplicity
of possibilities of further logical manipulations.
• A measurement scale should possess the following attributes
to allow for these logical manipulations
• Magnitude - quantity in which the attribute exists in various
instances of the phenomena.
• Equal intervals - It denotes that the magnitude of the
attribute represented by a unit of measurement on the scale
is equal regardless of where on the scale the unit falls.
• Absolute zero point - Is a value that indicates exists at that
point or nothing at all of the attribute being measured exists.
18. Types of Measurement
• 1. Nominal Scale (Classificatory Scale)
• It refers to the simple classification of objects or
items in to discrete groups which do not bear any
magnitude relations ships to one another.
• “Nominal” stands for “name” of category. The
nominal scale of measurement is used for
qualitative rather than quantitative data: blue,
green, red, male, female; marital status (married,
single, divorced, etc.); professional classification;
geographic classification; and so on.
19. • Ordinal Scale or Ranking Scale
• First it is nominal scale and here data elements may
be ordered according to their relative size or quality.
It means that ordinal scale is first of all, nominal but
most people would agree with the order in which
the categories were placed so, in ordinal scale
inequalities have a meaning the inequality signs ‘<’
or’>’ may assume any meaning like “strong than”,
“softer than”, “weaker than” etc.
• Ordinal scale reflects only magnitude and does not
possess the attribute of equal intervals or an
absolute zero point
20. • Interval Scale
• The interval scale possesses two out of three
important requirements of good measurement
scale i.e., magnitude and equal intervals but lacks
the real or absolute zero point.
• An interval scale is one, which provides equal
intervals from an arbitrary origin. An interval scale
not only orders according to the amount of the
attribute they represent, but also establishes equal
intervals between the units of measure. Equal
differences in the numbers represent equal
differences in the attribute being measured.
21. • Ratio Scale
• The scale of measurement which has all the three
attributes – magnitude, equal intervals and an
absolute zero point- is called a ratio scale addition,
subtraction, multiplication and division of the
numbers are appropriate
• Example 1.12:- All physical measurements, like
height, weight, etc.
- Number of students in various classes.
- Number of books possessed by students of a class, etc.
