Variables
• Categorical / Qualitative
– Classifies subject by an attribute or
characteristic.
– Hair color, type of professor, make of car
• Quantitative
– Gives numerical measures of subjects.
– Weight, height, response time, number of
miles traveled to work
Quantitative Variables
• Discrete
– A countable number of wholenumbered values (no
decimals).
• # of people entering a shop per hour (whole number)
• # of living grandparents (0,1,2,3,4 only)
• # of spades in a poker hand (0,1,2,3,4,5 only)
• # of balls a juggler is currently juggling
• Continuous
– Can take on any numerical value (including decimals) on
an interval.
• Weight of an athlete (150, 150.01, 181.312, etc)
• Time taken to complete a lap
• The current speed of an airplane
Examples (HW 1.12.1)
•
Which of these are categorical or quantitative? For the
latter, which are discrete or continuous?
•
Length of an earthworm (in mm)
•
Region of U.S. (Southeast, West, etc.)
•
Literary genre
•
Number of times in one month the Creswell fire alarm
goes off
Important Terms
• Population
– Total set of subjects in
which we are
interested
• Sample
– A subset of the
population for which
we have data
• Subject
– Entities we measure
(individuals)
Important Terms
• Parameter
– A numerical value summarizing the population
data.
– Ex: number of freshmen out of all STAT 2000
students
• Statistic
– A numerical value summarizing the sample data.
– Ex: number of freshmen out of a sample of 100
STAT 2000 students
• Statistic and Sample both begin with S
Example (HW 1.1 – 2.1)
• A college dean wants to know the average
age of the faculty. She takes a random
sample of 10 faculty members and
averages their ages.
•
Population
=
•
Sample
=
•
Subject
=
•
Parameter
=
•
Statistic
=
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View Full DocumentDescriptive vs. Inferential
• Descriptive Statistic
– Summary of the data in the sample.
• Majority of students in a sample of 1000 attend
UGA football games
• Inferential Statistic
– A conclusion or prediction about the
population based on the sample data.
• Majority of all UGA students attend UGA football
games, based on the sample
Frequencies
Example: 18 cookies out of a random sample of 32
are chocolate chip
proportion =
frequency
total number of observations
percentage =
frequency
total number of observations
!
"
#
$
%
’
100
proportion
=
18
32
=
.5625
percentage
=
18
32
!
"
#
$
%
’
100
=
56.25%
Frequencies (HW 2.12.2)
• Results from the question of how many
children a family has had. Fill in the answers.
• # Children
0
1
2
3
• Count
786
460
662
489
•
Proportion
•
Percentage
Types of Charts (Categorical)
Bar Graph
•
Categories on horizontal
axis, frequency on
vertical axis, height of
rectangle is frequency
Pareto Graph
•
A bar graph arranged with
bars in descending order
of frequency
Types of Charts (Categorical)
Pie Chart
•
A circle divided into
slices, with each slice
representing a category
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 Spring '10
 MORSE
 Statistics, Standard Deviation

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