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# Chapter 1.2 - PowerPoint PPT Presentation

Chapter 1.2. Variables and Types of Data. Variables. Qualitative variables are variables that can be placed into distinct categories, according to some characteristic or attribute. Example: gender Quantitative variables are numerical and can be ordered or ranked. Example: age.

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### Chapter 1.2

Variables and Types of Data

• Qualitative variables are variables that can be placed into distinct categories, according to some characteristic or attribute. Example: gender

• Quantitative variables are numerical and can be ordered or ranked. Example: age

• Marital status of teachers in the school

• Time it takes to complete a test

• Weight of tiger cubs at birth in a zoo

• Colors of cars for sale at a dealership

• SAT score

• Ounces of soda in a cup

Quantitative variables can be classified into two groups: discrete and continuous.

• Discrete variables assume values that can be counted. Example: number of students in a class

• Continuous variables can assume an infinite number of values between any two specific values. They are obtained by measuring. Often including fractions and decimals. Example: temperature

Continuous Variables Boundaries

• Measurement scales classify variables by how they are categorized, counted, or measured. Example: area of residence, height

• The four common types of scales that are used are:

nominal, ordinal, interval, and ratio

• Classifies data into mutually exclusive (nonoverlapping) categories in which no order or ranking can be imposed on the data

Examples:

• Gender

• Zip code

• Political party

• Religion

• Marital status

• Classifies data into categories that can be ranked, however, precise differences between the ranks do not exist

Examples:

• First, second, third place

• Superior, average, or poor

• Small, medium, or large

• Ranks data, and precise differences between units of measure do exist; however, there is no meaningful zero

• Different from ordinal because precise differences do exist between units

Examples:

• IQ (no zero because it does not measure people without intelligence)

• Temperature (no zero because temperature exists even at 0°)

• Possesses all the characteristics of interval measurement, and there exists a true zero. In addition, true ratios exist when the same variable is measured on two different members of the population

Examples:

• Height

• Weight

• Area

There is not complete agreement among statisticians about classification of data. And data can be altered so that they fit into different categories.

Examples:

• Income: low, medium high (ordinal) or \$100,00,

\$45, 000, etc. (ratio)

• Grade: A, B, C, D, F (ordinal) or 100, 90, 80, etc. (interval)

Try it! ratio

• Pg. 9 #1-7