This document introduces key concepts in statistics. It discusses descriptive statistics, which involves collecting, organizing and presenting data, and inferential statistics, which involves drawing conclusions about populations from samples. It also defines important statistical terms like population, sample, variables and different data types. Qualitative variables are variables that can be placed into categories, and include nominal and ordinal data. Quantitative variables can take numerical values and include discrete and continuous data. The document provides examples of each data type. It also discusses methods for collecting data and different sampling techniques like simple random sampling and stratified random sampling.

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Lecture: Ravaz Mohhamed Salih Alakrawi
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Lecture: Ravaz Mohhamed Salih Alakrawi
Introduction:
Statistics: is the science of data. It involves collecting , organizing
,presenting, analyzing , and interpreting numerical information .statistics is
used in several different disciplines (both scientific and non scientific) to make
dictions and draw conclusions based on data.
Statistics is that branch of science which deals ·with:
1. Collection of data,
2. Organizing and summarizing the data,
3. Analysis of data and
4. Making inferences, or decision and predictions.
The purpose of this chapter to introduce the goals for studying statistics by
answering questions such as the following:
What are the branches of statistics?
What are data?
How are samples selected?
There are two branch of statistics :
Descriptive and Inferential Statistics:
To gain knowledge about haphazard situations, statisticians collect information
for variables, which describe the situation.
A variable is a characteristic or attribute that'can assume different values.
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Data are the values (measurements or observations) that the variables can
assume. Variables whose values are determined by chance are called random
variables.
The statistics is divided into two main areas, The two areas are:
1. Descriptive statistics
2. Inferential statistics
Descriptive statistics: consists of the collection, organization and
presentation of data.
Inferential statistics: consists of generalizing from samples to populations,
Performing estimations and h y p o t h e s i s tests, d termining relationships
among variables, and making predictions.
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Population: consists o f all subjects (human or otherwise) that a r e b e i n g
studied.
Most of the time, due to the expense, time, size of population, medical
Concerns, etc., it is not possible to use the entire population for a statistical
study; therefore, researchers use samples.
A sample: is a group of subjects selected from a population.
If the subjects of a sample are properly selected, most of the time they should
possess the same or similar characteristics as the. Subjects in the population.
An area of inferential statistics called hypothesis testing is a decision-making
process for evaluating c l a i m s abo u t a population)., based on
information obtained from samples. For example, a researcher may wish to
know if a new drug will reduce the number of heart attacks in men over 70
years of age. For this study, two groups of men over 70 would be selected. One
group would be given the drug, and the other would be given a placebo (a
substance with no medical benefits or harm). Later, the number of heart attacks
occurring in each group of men would be counted, a statistical test would be
run, and a decision would be made about the effectiveness of the drug.
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Variables and Types of Data:
Statisticians gain information about a particular situation by collecting data for
random variables. This section will explore in greater detail the nature of
variables and types of data.
Variables can be classified as :
1. Qualitative variables: are variables that can be placed into distinct
categories, according to some characteristic or attribute. For
example, if subjects are classified according to gender (male or
female), then the variable gender is qualitative. Other examples of
qualitative variables are religious preference and geographic
locations.
Nominal variables: consists names, labels, or categories , gender
,major at college. There is no natural or obvious ordering of nominal
data (such as high to low), subject taught (e.g., English, history,
psychology, or mathematics), zip codes. Arithmetic cannot be carried
out on nominal data.
b. Ordinal variables: can be arranged in any particular order. Eg: area
of residence (rural, suburban, or urban); political party
(Democratic, Republican, Independent, etc.), religion (Christianity,
Juqaism, Islam, etc.), and marital status (single, married, divorced,
widowed, separated). However, no arithmetic can be done or
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performed on ordinal data.
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2. Quantitative variables can be further classified into two groups:
Discrete and continuous.
!:. Discrete variables: assume values that can be counted.
Examples of discrete variables are the number of children in a family, the
number of students in a classroom, and the number of calls received by a
switchboard operator each day for a month
b. Continuous variables: can assume an infinite number of values
between any two specific values. They are obtained by measuring.
They often include fractions and decimals..
The classification of variables can be summarized as follows chart:
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Example: Identify which type of data is represented by the following data:
Nominal data: (Zip code, Gender (male, female), Eye color (blue, brown,
green, hazel), Political affiliation, Religious affiliation, Major field
(mathematics, computers, etc.), Nationality.
Ordinal data: Grade (A, B, C, D, F), Judging (first place, second place,
etc.), Rating scale (poor, good, excellent), Ranking of tennis players.
Discrete data: years, Numbers on the back of each player's shirt, Top 50
songs played on the radio.
Continuous data: Height, Weight, Time, Salary, Age.
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Data Collection and Sampling Techniques:
Data can be collected in a variety of ways. One of the most common methods is
through the use of surveys. Surveys can be done by using a variety of methods.
Three of the most common methods are the telephone survey, the mailed
questionnaire, and the personal interview.
Simple Random Sampling:
Random samples are selected by using chance methods or random numbers.
One such method is to number each subject in thpopulation. Then place
numbered cards in a bowl, mix them thoroughly, and select as many cards as
needed. The subjects whose numbers are selected con titute the sample. Since
it is difficult to mix the cards thoroughly, there is a chance of obtaining a
biased sample. For this reason, statisticians use another method of obtaining
numbers. They generate random numbers with a computer or calculator. Before
the invention of computers, random numbers were obtained from tables.
<V Stratified Random Sampling:
IResearchers obtain stratified samples by dividing the population into groups
(called strata) according to some characteristic that is important to the study,
then sampling from each group. Samples within the strata should be randomly
For example, suppose the president of a two-year college wants to
learn how students feel about a certain issue. Furthermore, the presi ent wishes
to see if the opinions of the first-year students differ from those of the second
year students. The president will randomly select students from each group to
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use in the sample.
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