Elementary Statistics

Seventh Edition

Chapter 1
Introduction to
Statistics

Copyright 2019, 2015, 2012, Pearson Education, Inc. Slide 1

Chapter Outline
• 1.1 An Overview of Statistics

• 1.2 Data Classification

• 1.3 Data Collection and Experimental Design

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Section 1.2

Data Classification

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Section 1.2 Objectives
• How to distinguish between qualitative data and
quantitative data
• How to classify data with respect to the four levels
of measurement: nominal, ordinal, interval, and
ratio

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Types of Data (1 of 2)
Qualitative Data
Consists of attributes, labels, or nonnumerical entries.

Major Place of birth Eye color

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Types of Data (2 of 2)
Quantitative data
Numerical measurements or counts.

Age Weight of a letter Temperature

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Example: Classifying Data by Type
The table shows sports-related head injuries treated
in U.S. emergency rooms during a recent five-year
span for several sports. Sports-Related Head Injuries
Which data are qualitative Treated
Sport
in U.S. Emergency Rooms
Head injuries treated
data and which are Basketball 131,930
quantitative data? Baseball 83,522
Football 220,258
(Source: BMC Emergency Gymnastics 33,265
Medicine) Hockey 41,450
Soccer 98,710
Softball 41,216
Swimming 44,815
Volleyball 13,848

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Solution: Classifying Data by Type
Sports-Related Head Injuries
Treated in U.S. Emergency Rooms
Sport Head injuries treated
Basketball 131,930
Baseball 83,522
Football 220,258
Gymnastics 33,265
Hockey 41,450
Soccer 98,710
Softball 41,216
Swimming 44,815
Volleyball 13,848

Qualitative Data (Types Quantitative Data

of sports are (Head injuries treated
nonnumerical entries.) are numerical entries.)
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Levels of Measurement (1 of 3)
Nominal level of measurement
• Qualitative data only
• Categorized using names, labels, or qualities
• No mathematical computations can be made

Ordinal level of measurement

• Qualitative or quantitative data
• Data can be arranged in order, or ranked
• Differences between data entries is not meaningful
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Example: Classifying Data by Level
(1 of 2)
For each data set, determine whether the data are at the
nominal level or at the ordinal level. Explain your
reasoning. (Source: U.S. Bureau of Labor Statistics)
1. Top five U.S. occupations with the 2. Movie genres
most job growth (projected 2024) Action
1. Personal care aides
Adventure
2. Registered nurses
Comedy
3. Home health aides
Drama
4. Combined food preparation and
serving workers, including fast food Horror
5. Retail salespersons
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Solution: Classifying Data by Level
(1 of 2)
1. Top five U.S. occupations with the 2. Movie genres
most job growth (projected 2024)
Action
1. Personal care aides
Adventure
2. Registered nurses
Comedy
3. Home health aides
4. Combined food preparation and Drama
serving workers, including fast food Horror
5. Retail salespersons

Nominal level (lists movie

Ordinal level (lists the rank
genres). No mathematical
of five largest job growth
computations can be made
occupations. Data can be
and cannot be ranked.
ordered. Difference between
ranks is not meaningful.)
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Levels of Measurement (2 of 3)
Interval level of measurement
• Quantitative data
• Data can ordered
• Differences between data entries is meaningful
• Zero represents a position on a scale (not an
inherent zero – zero does not imply “none”)

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Levels of Measurement (3 of 3)
Ratio level of measurement
• Similar to interval level
• Zero entry is an inherent zero (implies “none”)
• A ratio of two data values can be formed
• One data value can be expressed as a multiple of
another

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Example: Classifying Data by Level
(2 of 3)
Two data sets are shown. Which data set consists of
data at the interval level? Which data set consists of
data at the ratio level?
(Source: Major League Baseball)

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Example: Classifying Data by Level
(3 of 3) 2016 American League
home run totals (by team)
Baltimore 253
Boston 208
New York Yankees’ Chicago 168
Cleveland 185
World Series victories (years)
Detroit 211
1923, 1927, 1928, 1932, 1936, Houston 198
1937, 1938, 1939, 1941, 1943, Kansas City 147
1947, 1949, 1950, 1951, 1952, Los Angeles 156
Minnesota 200
1953, 1956, 1958, 1961, 1962,
New York 183
1977, 1978, 1996, 1998, 1999, Oakland 169
2000, 2009 Seattle 223
Tampa Bay 216
Texas 215
Toronto 221

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Solution: Classifying Data by Level
(2 of 3)
New York Yankees’
World Series victories (years)
1923, 1927, 1928, 1932, 1936,
1937, 1938, 1939, 1941, 1943,
1947, 1949, 1950, 1951, 1952,
1953, 1956, 1958, 1961, 1962,
1977, 1978, 1996, 1998, 1999,
2000, 2009

Interval level (Quantitative

data. Can find a difference
between two dates, but a
ratio does not make sense.)
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Solution: Classifying Data by Level
(3 of 3) 2016 American League
home run totals (by team)
Baltimore 253
Boston 208
Chicago 168
Cleveland 185
Detroit 211
Houston 198
Kansas City 147
Los Angeles 156
Minnesota 200
New York 183
Oakland 169
Seattle 223
Tampa Bay 216
Texas 215
Toronto 221

Ratio level (Can find differences and write ratios.)

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Summary of Four Levels of
Measurement (1 of 3)
Put data Arrange Subtract Determine if one
Level of in data in data data value is a
Measurement categories order values multiple of another
Nominal Yes No No No
Ordinal Yes Yes No No
Interval Yes Yes Yes No
Ratio Yes Yes Yes Yes

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 Summary of Four Levels of
Measurement (2 of 3)
blank cell Example of a data set Meaningful calculations
Nominal Types of Shows Televised by a Network Put in a category.
level Comedy Documentaries For instance, a show
(Qualitative Drama Cooking televised by the network
data) Reality Shows Soap Operas could be put into one of the
Sports Talk Shows eight categories shown.
Ordinal level Motion Picture Association of America Ratings Put in a category and put in
(Qualitative or Description order.
quantitative G General Audiences For instance, a PG rating
data) PG Parental Guidance Suggested has a stronger restriction
PG-13 Parents Strongly Cautioned than a G rating.
R Restricted
NC-17 No One 17 and Under Admitted

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Summary of Four Levels of
Measurement (3 of 3)

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