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Chapter 1.2

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

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  1. Chapter 1.2 Variables and Types of Data

  2. 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

  3. Classify each variable as Quantitative or Qualitative • 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

  4. Discrete vs. Continuous 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

  5. Continuous Variables Boundaries

  6. Measurement Scales • 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

  7. Nominal Level of Measurement • 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

  8. Ordinal level of Measurement • 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

  9. Interval level of Measurement • 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°)

  10. Ratio level of Measurement • 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

  11. Agreement? 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)

  12. Classify each variable as nominal, ordinal, interval, or ratio

  13. Try it! • Pg. 9 #1-7

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