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What is Statistics?
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When you have completed this chapter, you will be able to:
1.
Explain what is meant by statistics.
2.
Identify the role of statistics in the development
of knowledge and everyday life.
3.
Explain what is meant by descriptive statistics
and inferential statistics.
4.
Distinguish between a qualitative variable and
a quantitative variable.
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5.
Distinguish between a discrete variable and
a continuous variable.
6.
Collect data from published and unpublished sources.
7.
Distinguish among the nominal, ordinal, interval,
and ratio levels of measurement.
8.
Identify abuses of statistics.
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9. Gain an overview of the art and science of
statistics.
We recommend that you read this chapter at least
twice, once at the beginning and once at the end of
your course!
Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
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…it is the art and science of…
 collecting
What
is
Meant
by
Statistics?
 organizing
 presenting data
 drawing inferences
from a sample of
information
about an entire population
as well as
 predicting and
developing policy analysis
Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
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in everyday life
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Who uses Statistics?
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Those using Statistical techniques include :
Marketers
Accountants
Hospitals
Investors
Sports people
Statisticians
Economists
Consumers
Quality Controllers
Educators
Politicians
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Physicians
Who uses Statistics?
Weather
Forecasters
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Who uses Statistics?
Sports
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Types of
Statistics
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Types of Statistics
Descriptive
Methods of…
collecting
organizing
presenting
and
analyzing data
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Inferential
Science of…
making inferences
about a population,
based on sample
information.
Identify the following…
Descriptive
A. A Gallup poll found that 83%
of the people in a survey knew
which country won the gold
medal in Men’s Hockey in 2002.
B. The accounting department of
a firm will select a sample of
invoices to check for accuracy of
all the invoices of the company.
C. Wine tasters sip a few drops
of wine to make a decision
with respect to all the wine
waiting to be released for sale.
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Inferential
The Method of
Experimentation
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We start off with particular observations
from the real world and
draw conclusions
about the general patterns in the real world!
1.
2.
3.
4.
5.
6.
Define the experimental goal or a working hypothesis
Design an experiment Chapters 8 and 12
Collect data
Estimate the values/relations Chapters 3 and 4
Draw inferences
Chapters 5,7,8 and 9
Predict and prepare policy analysis
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Inductive Method
The Method of
Experimentation
Statistical
Techniques
Experimental Design &
Data Collection
Objective
or WorkingHypothesis
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Deductive Method
Definitions &
Assumptions
Rules of
Logic
Implications or
Hypotheses
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A study was undertaken to estimate the
average height of penguins in Antarctica.
Let’s review the steps they would take to
prepare the estimate.
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A population is a
collection of
all possible individuals,
objects,
or
measurements of interest
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From
Take a
…which are deemed to be representative of the
What we now need is…
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Take a
Measurement
for each one
in the sample
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Record
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…to put the data
into a
readable and
understandable
format!
Displaying Data Results
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Two methods that can be
used to ‘see’
what the data conveys are
Tables and
Graphs/Charts
More on these in chapter 2…
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Tables
… are an efficient method of displaying data
and depicting data accurately.
e.g.
More on these in chapter 2…
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Line
Pie
Bar
More on these in chapter 2…
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Why take a sample instead of studying
every member of the population?
Costs of surveying the entire population
may be too large or prohibitive
Destruction of elements during
investigation
Accuracy of results
More in chapter 8 …
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Data are everywhere
Statistical techniques are used to make
many decision that affect our lives
No matter what your future line of
work, you will make decisions that
involve data. An understanding of
statistical methods will help you make
these decisions more effectively.
Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
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Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
Types of Data
A Variable
a characteristic
of
a population or sample
that is of interest to us
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Types of Data
Variables
Qualitative
Categorical
Observations
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Quantitative
Numerical
Observations
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Variables
Qualitative – or Attribute
Country of Birth
U.K.
Eye Colour
Germany
Blue
Gender
Taiwan
Brown
Male
China
Hazel
India
Female
Green
Japan
Red
Russia
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Variables
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Quantitative – Numeric
Minutes to end
of Class
55
Number of
45
Children in
30
a Family
5
0
1
2
3
4
…
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Number of
Two-Door
Number of
Garages
Satisfied
in a Street Maple Leafs Fans
10
20
30
40
...
0
20
30
40
…
Variables
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Quantitative … can be classified as either
Numerical
Observations
Discrete
Discrete or
Continuous
Characteristics
… can only assume certain values
and
there are usually “gaps” between values
e.g. - Number of bedrooms in a house
- Number of hammers sold (1,2,3,…etc)
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Variables
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Quantitative … can be classified as either
Discrete or
Continuous
Numerical
Observations
Characteristics
Continuous
… can assume any value
within a specified range!
e.g. - Pressure in a tire
- Weight of a pork chop
- Height of students in a class
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Summary of Types of Variables
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Data
Qualitative
Quantitative
Categorical
Observations
Numerical
Observations
Discrete
Continuous
(number of children)
(time used for an exam)
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Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
Sources of Statistical
Information
Published Data
Statistical Abstracts
Weather
Sports
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Sources of Statistical
Information
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www.strategis.gc.ca
www.bankofcanada.ca
Internet
Government of
Canada & Provinces www.gc.ca
www.theweathernetwork.com
www.mcgrawhill.ca/college/lind
Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
Sources of Statistical
Information
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www.census.gov
www.bls.gov/
International
www.un.org
Organization for
Economic
Co-operation and
Development
www.oecd.org
IMF
www.imf.org
www.worldbank.org/
Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
Sources of Statistical
Information
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Commissioned surveys:
To develop information for the survey that they are
doing, pollsters often contact the selected
‘sample population’.
For Example…At home, over the telephone, by
mail, by email, in the street, and at shopping malls!
How to collect data…
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Nominal
Ordinal
Interval
Ratio
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Nominal
Data can only be classified into categories or counted
and cannot be arranged in any particular order
Example
M & Ms
Category:
Candy
Classification:
By Colour only
(No natural order)
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Nominal
Example
M & Ms
Mutually Exclusive:
…where an individual, object, or measurement is
included in ONLY ONE CATEGORY
Exhaustive:
…where each individual, object, or measurement
MUST APPEAR in one of the categories
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Ordinal
…involves data arranged in some order,
but
the differences between data values
cannot be determined or are meaningless!
Example
During a taste test of 4 soft drinks:
Mello Yello was ranked number……..…. 1.
Sprite number……………………………. 2.
Seven Up number..…………………..…... 3.
Orange Crush number ….……………….4.
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Interval
…similar to the Ordinal Level,
with the additional property
that meaningful amounts of differences between data
values can be determined.
There is no natural zero point
Example
Temperature on the Celsius scale.
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Ratio
…the Interval Level with an inherent zero
starting point.
Differences and ratios are meaningful
for this level of measurement.
Examples
Monthly income
Distance
of surgeons
travelled by manufacturer’s
representatives per month
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Benjamin Disraeli said…
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“There are three kinds of lies…
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Figures don’t lie.
Liars figure!
Caution
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Caution
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As you begin to study statistical methods,
you are cautioned to take what you see published as
“statistical facts”
with a healthy grain of skepticism!
… an average may not be representative of all the data
… graphs can also be misleading
… be sure to study the sampling methods
For Example
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Caution
Review the following three slides and
notice the effect
that the
different scales
have on your interpretation of the
pattern between
Crime and Unemployment Rates.
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Chart 1-11A
1986 - 1999
3200
3000
2800
2600
2400
2200
2000
0 1 2 3 4 5 6 7 8 9 10 11 12
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Chart 1-11B
1986 -1999
3000
2500
2000
1500
1000
500
0
7
8
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9
10
11
12
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Chart 1-11C
1986 -1999
3200
3000
2800
2600
2400
2200
2000
7
8
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11
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Test your learning…
www.mcgrawhill.ca/college/lind
Online Learning Centre
for quizzes
extra content
data sets
searchable glossary
access to Statistics Canada’s E-Stat data
…and much more!
Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
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This completes Chapter 1.
Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.