EXAMPLES:
balance in your checking account, minutes
remaining in class, or number of children in a family.
8
Quantitative Variables - Classifications
Quantitative variables can be classified as either
discrete
or
continuous
.
A. Discrete variables
:
can only
assume certain values
and there are
usually “gaps”
between values.
EXAMPLE
:
the number of bedrooms in a house, or the number of hammers sold at the local
Home Depot (1,2,3,…,etc).
B.
Continuous variable
can
assume any value
within a
specified range.
EXAMPLE:
The pressure in a tire, the weight of a pork chop, or the height of students in a
class.
8

Summary of Types of Variables
9
MONEY
as
Continuous
Data
By definition (for this course) a
monetary value
(i.e.
money
) is classified as
continuous
:
z
Income
z
Rent
z
Purchase Price
The only time money is considered discrete is if you
are counting a number of
things
:
z
Number of silver dollars
z
Number of pennies in your pocket
I will
not
try to trick you

Four Levels of Measurement
Nominal level -
data that is classified
into categories and cannot be
arranged in any particular order.
EXAMPLES:
eye color, gender,
religious affiliation.
Ordinal level –
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,
Mellow Yellow was ranked number 1, Sprite
number 2, Seven-up number 3, and Orange
Crush number 4.
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.
Ratio level -
the interval level with an
inherent zero starting point.
Differences and ratios are
meaningful for this level of
measurement.
EXAMPLES:
Monthly income of surgeons, or
distance traveled by manufacturer’s
representatives per month.
9
Nominal-Level Data
Properties:
1.
Observations of a qualitative variable can only be
classified
and
counted
.
2.
There is
no particular order
to the labels.
10

Identifying Categories Completely
For categories to be sufficient (e.g. for use when
setting up a frequency table) the categories must
Mutually Exclusive
and
Collectively Exhaustive
z
Categories are
mutually exclusive
if an observation can
only belong to one of the categories.
z
Categories are
collectively exhaustive
if every possible
observation can be matched to an appropriate category.
Ordinal-Level Data
Properties:
1.
Data classifications are
represented by sets of labels or
names (high, medium, low) that
have
relative values
.

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- Spring '11
- Leany
- Statistics, Inferential Statistics, Level of measurement