Statistics (Stat 101)
Sameh Saadeldin Ahmed
Associate Professor of Environmental Eng.
Civil Engineering Department
Engineering College
Almajma’ah University
smohamed1@ksu.edu.sa
http://faculty.ksu.edu.sa/SaMeH
Stat 101
2010/2011
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Chapter 1
Week
1st
week
Stat 101
Subject
Content
Concepts of •What is Statistics?
Probability •Types of Statistics
and Statistics
•Basic Terms
•Types of Variables
•Sources of Data
•Data Collection
Approaches
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1.0 Introduction
The study of statistics has become more
popular than ever over the past four decades.
The increasing availability of computer and
statistics software packages has enlarged the
role of statistics as a tool for empirical
research. As a result, statistics is used in
almost all professions, from medicine to
sports.
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1.1 What is Statistics?
Statistics is a group of methods used to
collect, analyze, present and interpret
data and to make decisions.
Statistics has two aspects:
•
•
Stat 101
Theoretical or mathematical statistics:
deals with the development, derivation, and
proof of statistical theorems, formulas, rules and
laws.
Applied statistics: involves the applications
of those theorems, formulas, rules and laws to
solve real-world problems.
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1.2 Types of Statistics
1.2.1 Descriptive Statistics:
Descriptive statistics consists of methods for
organizing, displaying, and describing data
by using tables, graphs and summary
measures.
1.2.2 Inferential Statistics:
Inferential statistics consists of methods that
use the sample results to help make decisions
or predictions about a population.
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Probability, which gives a measurement of
the likelihood that a certain outcome will
occur, acts as a link between descriptive
and inferential statistics.
Descriptive
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Probability
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Inferential
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1.3 Population versus Sample
Ex: suppose a statistician is interested in knowing:
•
•
•
The percentage of all voters in a college who will
vote for a particular candidate in an election.
The 1429 gross sales of all companies in
Almajmaah city.
Prices of all houses in Riyadh.
Each of these groups is called the population
for the respective example.
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Definition of Population
Population or Target Population:
A population consists of all elements –
individuals, items, or objects – whose
characteristics are being studied.
The population that being studied is also called
target population.
Most of the time, decisions are made based on portions
of populations.
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Example: the election polls conducted in United
States to estimate the percentage of voters who
favour various candidates in any presidential
election are based on only a few hundred of a
few thousand voters selected from across the
country.
In this case, the population consists of all
registered voters in US. The sample is made up
of the few hundred or few thousand voters who
are included in an opinion poll. Thus, the
collection of a few elements selected from a
population is called a sample.
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Definition of Sample
Sample:
A portion of the population selected for study is
referred to as a sample.
Population
Sample
Population and sample
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Census and Representative sample
Census and Sample Survey:
A survey that includes every member of
population is called census.
The technique of collection information from a
portion of the population is called a sample
survey.
Representative Sample:
A sample that represents the characteristics of
the population as closely as possible is called a
representative sample.
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Example: to find the average income of
families living in Riyadh city by
conducting a sample survey, the sample
must contain families who belong to
different income groups in almost the
same portion as they exist in the
population.
Such a sample is called representative sample.
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Random and Non-random
Random Sample:
A sample drawn in such a way that each element
of the population has a chance of being selected
is called random sample.
Sampling with replacement and sampling without replacement..
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With and Without Replacement
red
blue
green
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Exercises
1.
Briefly explain the terms: population, sample,
representative sample, random sample, sampling
with replacement and sampling without replacement.
2.
Explain whether each of the following constitutes a
population or a sample?
• Credit card debt of 100 families selected from a
city
• Ages of all members of a family
• Amount spent on prescription drugs by 200 senior
citizens in a large city.
• Scores of all students in statistics in statistics class.
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1.4 Basic Terms
This section explains the meaning of:
 an element,
 a variable,
 an observation, and
 a data set.
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Table 1.1: Students enrolment at six engineering departments
Observation
Element
or
Member
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Variable
Department No. of students
Civil
Mechanical
Electrical
Industrial
Mining
Chemical
420
140
330
50
50
70
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Element and Variable
Element or Member:
An element or member of a sample or
population is a specific subject or object (for
example, a person, firm, item, state, or country)
about which the information is collected.
Variable
A variable is a characteristic under study that
assumes different values for different elements.
In general, a variable assumes different values for different elements,
as in table 1.1, for some elements in a data set; however,
the value of the variable may be the same.
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Observation and Data set
Observation or Measurement:
The value of a variable for an element is called
observation.
Data Set
A data set is a collection of observations on one
or more variables.
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1.5 Types of Variables
A variable may be classifies as quantitative or
qualitative:
1.5.1 Quantitative Variables
Example: incomes, heights, gross sales, prices of
houses, number of accidents are examples of
quantitative variables because each of them
can be expressed numerically.
1.5.2 Qualitative Variables
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1.5.1 Quantitative Variable
Quantitative Variable:
A variable that can be measured numerically is
called quantitative variable.
Quantitative variables classified to:
discrete variables and
continuous variables.
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Discrete Variable
Discrete Variable :
A variable whose values are countable.
A discrete variable can assume only certain values with no
intermediate values.
Examples:
 The number of cars sold on any day at a car dealership is
a discrete variable. (5, 6,…. Not 5.3!!).
 Number of people visiting a bank on any day.
 Number of cars in a parking area.
 Number of students in a class
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Continuous Variable
Continuous Variable :
A variable that can assume any numerical value
over a certain interval or intervals.
Some variables can not be counted, and they assume any
numerical value between two numbers such variables are
called continuous variables.
Examples:
 The time taken to complete an examination (30 -60min.).
 Weight of people (kg, gm, ..).
 Height of people (cm, mm, ..)
Note) any variable that involves money is considered a
continuous variable.
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1.5.2 Qualitative Variable
Qualitative Variable:
A variable that can not assume a numerical
value but can be classified into two or more
nonnumeric categories is called qualitative
variable.
Examples:
 Gender of person (M/F).
 Hair colour.
 The make of a car.
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1.5 Types of Variables
Types of variables
Qualitative
Quantitative
Discrete
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Continuous
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1.6 Cross-section versus
Time-series Data
1.6.1 Cross-section Data:
Data collected on different elements at the same
point in time or for the same period of time.
Example: the information on incomes of 50 families
for 2005.
1.6.2 Time-series Data
Data collected on the same element for the same
variable at different points in time or for
different period of time.
Example: KSA exports for the years 1990-2000.
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1.7 Sources of Data
Data may be obtained from
1. internal sources,
2. external sources, or
3. surveys and experiments
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1.8 Summation Notation
The following table lists four pairs of m and f
values. Compute the following:
a)  m
b)  f2
c)  m f
d)  m2 f
m
f
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5
15
9
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10
30
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End of Part 1
Get ready for a quiz (1)……
Next Lecture!!
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