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1.2 Data Classification Qualitative Data consist of attributes, labels, or non-numerical entries. – Examples are bigger, color, names, etc. Quantitative.

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Presentation on theme: "1.2 Data Classification Qualitative Data consist of attributes, labels, or non-numerical entries. – Examples are bigger, color, names, etc. Quantitative."— Presentation transcript:

1 1.2 Data Classification Qualitative Data consist of attributes, labels, or non-numerical entries. – Examples are bigger, color, names, etc. Quantitative data consist of numerical measurements or counts. – Examples are height in inches, color as wave length, etc.

2 Examples from the book Try it Yourself – pg 8 – A: City population – B: City: Non-numerical – B: Population: numerical – C: City: Qualitative – C: Population: Quantitative

3 Four levels of measurement, in order from lowest to highest: 1.Data at the nominal level of measurement are qualitative only. Data at this level are categorized using names, labels, or qualities. No mathematical computations can be made at this level. Examples are # on jerseys, SS #, phone numbers, team names, city names

4 Four levels of measurement, in order from lowest to highest: 2.Data at the ordinal level of measurement are qualitative or quantitative. Data at this level can be arranged in order, but differences between data entries are not meaningful., i.e., the difference between the positions has no mathematical meaning. Examples: ranking of TV shows, ranking of football teams, etc.

5 Four levels of measurement, in order from lowest to highest: 3.Data at the interval level of measurement are quantitative. The data can be ordered and you can calculate meaningful differences between data entries. At the interval level, a zero entry simply represents a position on a scale; the entry is not an inherent zero. Examples: temperature in F and C, year

6 Four levels of measurement, in order from lowest to highest: 4.Data at the ratio level of measurement are similar to data at he interval level, with the added property that a zero entry is an inherent zero. A ratio of two data values can be formed so one data value can be expressed as a multiple of another. Examples: temperature in K, height, money, points earned in a game, distance, anything that the expression “twice as much” has any meaning.

7 Examples from the book Try it Yourself – pg 9 1a) The final standings represent a ranking of hockey teams 1b) Ordinal 2a) The collection of phone numbers represent labels 2b) Nominal

8 Examples from the book Try it Yourself – pg 10 1a) The collection of body temperatures represent data that can be ordered, but makes no sense written as a ratio. 1b) Interval 2a) The collection of heart rates represent data that can be ordered and written as a ratio that makes sense. 2b) Ratio

9 Summary Look at page 11 of the book.

10 Homework: pg 12 – 13, # 1 – 20 all


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