The Nature of Probability and

Statistics

Dr. Nermin Fahmy

 Introduction

 Statistics: is the science of conducting

studies to
collect,
organize,
summarize,
analyze, and
draw conclusions from data.

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 1-1 Descriptive and Inferential Statistics
A variables a characteristic or attribute that can assume
different values.

Variables whose values are determined by chance are

called Random Variables.

The values that a variable can assume are called Data.

A collection of data values forms A data set.

Each value in the data set is called A data value or a

datum.
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 For example: A data set in table

Variables

id gender age Nationality

Data 1 male 20 Saudi
Set Data
2 male 25 Yemeni
3 female 30 Egypt
4 male 23 Jordanian
5 female 30 Lebanese Data
value

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 Sample Population

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A population consists of all subjects (human or otherwise)
that are studied.

A sample is a subset of the population( is a group selected

from a population).

For example :
In order to study the response times for emergency
988 calls in Jeddah 50 calls are selected randomly
over a six month period and the response times are
recorded .

Population : all calls 988.

Sample : 50 calls .
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 Branches of Statistics

Descriptive Inferential
statistic statistic
consists of consists of generalizing from
the collection, samples to populations, performing
organization, estimations and hypothesis tests,
summarization, determining relationships among
and presentation of data. variables, and making predictions.
For example : For example:
-the average age of the student is 14 the relationship between smoking
years. and lung cancer.
-the median household income for
people aged 25-34 is 35.888$.  probability .
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Variables and Types of Data
,Measurement Scales

Dr. Nermin Fahmy

 Variables and Types of Data
Variables

Qualitative
Categorical Quantitative
Non numerical Numerical

Discrete
Continuous
Countable
Can be decimals
5, 29, 8000, etc.
2.59, 312.1, etc.

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 Types of Variables

Qualitative Quantitative
Variables: variables

are variables that can be placed are numerical and can be

into distinct categories , ordered or ranked.
according to some
For example: Age ,Height ,
characteristic or attribute. Weight ,temperature …..etc

For example: Gender ,Marital

status ,Color……etc

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 Quantitative variables
classified into two groups

Discrete Continuous
Variables Variables
assume values that can be assume an infinite
counted . number of values between
any two specific values.
For example:
 Number of children in a family , For example:
Number of student in classroom, Temperature ,
 Number of DVDs rented Height
each day ……etc Weight
 Time …..etc

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 Measurement Scales

Qualitative Quantitative

Nominal Ordinal Interval Ratio

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 Measurement Scale of Qualitative

Nominal Ordinal
level level:
classifies data into mutually classifies data into
exclusive , exhausting categories can be ranked .
categories in which no order
or ranking can be imposed on For example:
the data. Grade of course (A,B,C) ,
For example: Size( S,M,L)
Rating scale (Poor ,Good
Eye color ,Gender ,
,Excellent )
Political party , blood types …etc
Ranking of tennis players …etc

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 Measurement Scale of Quantitative

Interval Ratio
level level

ranks data and precise possesses all the

differences between units of characteristics of interval
measure do exist ,however and there exist a true zero.
there is no meaningful zero.
0 +
For example: For example:
Temperature , - 0 + Height , Weight, Time,
IQ test…etc Salary , Age …etc

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 Data Collecting
and
Sampling Techniques

Dr. Nermin Fahmy

 Data collection

Observational
Survey and
Experimental

Mailed
Telephone Personal
questionnaire
surveys interview
surveys

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To obtain samples that are unbiased ,statisticians use four
methods of sampling:

Some Sampling Techniques

Random Stratified
sampling sampling

Systematic Cluster
sampling sampling

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 Summer Term

1- Random sampling
are selected by using chance methods or
random numbers

For example:

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 Summer Term

Q: Select random sample of 15 subjects out of 85 subjects:

A: 12, 27, 75, 62, 57, 13, 31, 06, 16, 49, 46, 71, 53, 41, 02

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 Summer Term

2- Systematic sampling
are obtained by numbering each value in the
population and then selecting the kth value.

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 Summer Term

3- Stratified sampling
are selected by dividing the population into
groups (strata) according to some characteristic
and then taking samples from each group.

For example:

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 Summer Term
A researcher select a random sample from each gender
to check their blood pressure

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 Summer Term

4- Cluster sampling
are selected by dividing the population into
groups and then taking samples of the groups .

For example:

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 Summer Term

Randomly Randomly
selected 2 selected 4
clusters clusters

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 Summer Term

1) Random : random number generator.

2) Systematic :every kth subject.

3) Stratified :divide population into group called “strata”.

4) Cluster :use intact groups.

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 Types of Studies

Observational Experimental
Study Study
The researcher merely observes The researcher manipulates one
what is happening or what has of the variables and tries to
happened in the past and tries to determine how the manipulation
draw conclusions based on these influences other variables.
observations. For examples:
For example:  Patients were randomly assigned to
 people who sleep 8 hours report two groups was given drug A and the
better health. other group was given drug B to
determine if the drug has an effect on
patient’s blood pressure.
A researcher counts the number of
 An Instructor has Three Teaching
people living in each house in method ,he want to apply a best
specific a street . method by seeing students grades.

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 Any Experiment has 2 Variables

Independent Variable Dependent Variable

or or
Explanatory Variable Outcome Variable

(or input) variable is the one that is the resultant variable

is being manipulated by the
researcher.

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 Independent temperature of exercise
water
Dependent time to cook an health
egg

Note :Statistical studies usually include one or more

independent variables and one dependent variable.

For example:
exercise , diet ( independent )
Influence
Health (dependent)
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 Types of group

Treatment Group
Control Group

The group that received the The group that dose not received
special instruction . the special instruction.

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 Chapter Definitions
1- Statistics 11-Qualitative Variables:
2- A variables 12-Quantitative variables
3-Random Variables. 13- Discrete Variables
4-Data. 14-Continuous Variables
5-A data set 15-Nominal level
6-A data value or a datum. 16- Ordinal level
7-A population 17-Interval level
8-A sample 18-Ratio level
9-Descriptive 19-Observational Study
10- Inferential 20- Experimental Study
21-Independent Variable 23-Treatment Group
22- Dependent Variable 24- Control Group

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