A Poem

1
A Poem

• The information you have is not the information
  you want
• The information you want is not the information
  you need
• The information you need is not the information
  you can obtain
• The information you can obtain costs more than
  you want to pay

2
What is Meant by Statistics?
1-2

• Statistics is the science of collecting,
  organizing, presenting, analyzing, and
  interpreting numerical data for the purpose of
  assisting in making a more effective decision.

3

• If you torture data hard enough, the data will
  confess whatever you are willing to hear.
• ------ Ronald Coase (Nobel Laureate)

4
Who Uses Statistics?
1-3

• Statistical techniques are used extensively by
  marketing, accounting, quality control,
  consumers, professional sports people, hospital
  administrators, educators, politicians,
  physicians, etc...

5
Types of Statistics
1-4

• Descriptive Statistics Methods of organizing,
  summarizing, and presenting data in an
  informative way.
• EXAMPLE 1 A Gallup poll found that 49 of the
  people in a survey knew the name of the first
  book of the Bible. The statistic 49 describes the
  number out of every 100 persons who knew the
  answer.
• EXAMPLE 2 According to Consumer Reports,
  Whirlpool washing machine owners reported 9
  problems per 100 machines during 1995. The
  statistic 9 describes the number of problems out
  of every 100 machines.

6
Types of Statistics
1-5

• Inferential Statistics A decision, estimate,
  prediction, or generalization about a population,
  based on a sample.
• A population is a collection of all possible
  individuals, objects, or measurements of
  interest.
• A sample is a portion, or part, of the population
  of interest.

7
Types of Statistics(examples of inferential
statistics)
1-6

• EXAMPLE 1 TV networks constantly monitor the
  popularity of their programs by hiring Nielsen
  and other organizations to sample the preferences
  of TV viewers.
• EXAMPLE 2 The accounting department of a large
  firm will select a sample of the invoices to
  check for accuracy for all the invoices of the
  company.
• EXAMPLE 3 Wine tasters sip a few drops of wine
  to make a decision with respect to all the wine
  waiting to be released for sale.

8
Types of Variables
1-7

• Qualitative or Attribute variable the
  characteristic or variable being studied is
  nonnumeric.
• EXAMPLES Gender, religious affiliation, type of
  automobile owned, state of birth, eye color.

9
Types of Variables
1-8

• Quantitative variable the variable can be
  reported numerically.
• EXAMPLE balance in your checking account,
  minutes remaining in class, number of children in
  a family.

10
Types of Variables
1-9

• Quantitative variables can be classified as
  either discrete or continuous.
• Discrete variables can only assume certain
  values and there are usually gaps between
  values.
• EXAMPLE the number of bedrooms in a house.
  (1,2,3,..., etc...).

11
Types of Variables
1-10

• Quantitative Variables can be classified as
  either discrete or continuous.
• Continuous variables can assume any value within
  a specific range.
• EXAMPLE The time it takes to fly from Toledo to
  New York.

12
Summary of Types of Variables
1-11
13
Sources of Statistical Data
1-12

• Researching problems usually requires published
  data. Statistics on these problems can be found
  in published articles, journals, and magazines.
• Published data is not always available on a given
  subject. In such cases, information will have to
  be collected and analyzed.
• One way of collecting data is via questionnaires.

14
Levels of Measurement
1-13

• Nominal level (scaled) Data that can only be
  classified into categories and cannot be arranged
  in an ordering scheme.
• EXAMPLES eye color, gender, religious
  affiliation.

15
Levels of Measurement
1-14

• Mutually exclusive An individual or item that,
  by virtue of being included in one category, must
  be excluded from any other category.
• EXAMPLE eye color.
• Exhaustive each person, object, or item must be
  classified in at least one category.
• EXAMPLE religious affiliation.

16
Levels of Measurement
1-15

• Ordinal level involves data that may be arranged
  in some order, but differences between data
  values cannot be determined or are meaningless.
• EXAMPLE During a taste test of 4 colas, cola C
  was ranked number 1, cola B was ranked number 2,
  cola A was ranked number 3, and cola D was ranked
  number 4.

17
Levels of Measurement
1-16

• Interval level 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 Fahrenheit scale.

18
Levels of Measurement
1-17

• Ratio level the interval level with an inherent
  zero starting point. Differences and ratios are
  meaningful for this level of measurement.
• EXAMPLES money, heights of NBA players.

19
Anecdotal evidence vs. representative evidence

• Anecdotal evidence is based on haphazardly
  selected individual cases
• Representative evidence is usually based on
  randomly selected evidence.

20
Observational studies vs. Experiments

• Basic idea in the observational study the
  researcher collects the data as they currently
  are, he or she is not in charge of assignment.
  In other words, the researcher cannot assign a
  treatment so for these kinds of studies there are
• No true treatment groups
• No true control
• Observational studies are inexpensive and do not
  require as much thoughtfulness.

21
Experiments and their design

• Treatment (an intervention)
• Control (what if we never intervened)
• A measurable response (a real outcome)

22
Even when we are clever, there may still be
problems

• - Confounding the effect of an unforeseen
  characteristic, behavior, event or procedure on
  the response that cannot be distinguished from
  the proposed treatment.

23
Solutions

• Randomization eliminates bias
• Placebo eliminates the placebo effect
• Double Blind eliminates bias
• Blind may eliminate bias
• Replication validate results

24
Review of Experiments

• Key ideas the researcher is able to assign a
  treatment and observe an outcome
• Strength able to control for potential
  confounding factors
• Best Method Random assignment to treatment or
  control group
• Goal to compare outcomes between groups
• Notes replication offer strength when
  placebo/blinding is impossible

25
Sampling Design

• Population Vs. Sample
• Ways to create sample
• Voluntary response sample
• Simple random sample (SRS)
• Stratified random sample
• Multistage sample

26
1-1
To Sum Up
GOALS When you have completed this chapter, you
will be able to
ONE Define what is meant by statistics. TWO Explai
n what is meant by descriptive statistics and
inferential statistics. THREE Distinguish between
a qualitative variable and a quantitative
variable. FOUR Distinguish between a discrete
variable and a continuous variable. FIVEDistingui
sh among the nominal, ordinal, interval, and
ratio levels of measurement.Define the terms
mutually exclusive and exhaustive. SIX Understand
the difference between observational study and
experiments SEVEN Understand the sources of data,
how to design an experiment