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What Are We Summarizing?

What Are We Summarizing?. Lecture 11 Sections 4.1 – 4.2 Tue, Sep 20, 2005. What Are We Summarizing?. There are various types of data. How the data are summarized depends on the type of data. See Data Set 1, p. 212. How best to summarize Gender? How best to summarize Age?

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What Are We Summarizing?

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  1. What Are We Summarizing? Lecture 11 Sections 4.1 – 4.2 Tue, Sep 20, 2005

  2. What Are We Summarizing? • There are various types of data. • How the data are summarized depends on the type of data. • See Data Set 1, p. 212. • How best to summarize Gender? • How best to summarize Age? • How best to summarize Blood Pressure?

  3. Qualitative Variables • Qualitative variable – A variable whose values are not numerical, but can be divided into categories. • The values of a qualitative variable may or may not have a natural order. • Examples: • Gender. • Questionnaire response, from strongly agree to strongly disagree.

  4. Quantitative Variables • Quantitative variable – A variable whose values are numerical. • A quantitative variable may be continuous or discrete.

  5. Continuous Variables • Continuous variable – The set of theoretically possible values of the variable forms a continuous set of real numbers. • Typically these are measured quantities: length, time, area, weight, etc. • Example: The length of time a student takes to complete a test. • Usually the noun does not have a plural form.

  6. Discrete Variables • Discrete variable – The set of theoretically possible values of the variable forms a set of isolated points on the number line. • Typically this is count data; a verbal description usually contains the phrase “the number of.” • Example: The number of students who completed the test within 40 minutes. • Usually the noun has a plural form.

  7. Discrete vs. Continuous • Some data may be considered to be either discrete or continuous. • Example: Time vs. Minutes. • How much time do I have for the test? • How many minutes do I have for the test? • Example: Money vs. Dollars. • How much money is in your pocket? • How many dollars are in your pocket? • In such cases, consider it to be continuous.

  8. Discrete vs. Continuous • Some data may be considered to be either discrete or continuous. • Example: Time vs. Minutes. • How much time do I have for the test? • How many minutes do I have for the test? • Example: Money vs. Dollars. • How much money is in your pocket? • How many dollars are in your pocket? • In such cases, consider it to be continuous.

  9. Discrete vs. Continuous • The distinction is based on the nature of the variable, not the manner in which it is measured or recorded. • Example: Measure the time it takes each student to finish a test, to the nearest minute. • The possible times are 0, 1, 2, 3, … minutes. • Is that discrete or continuous?

  10. Let’s Do It! • Let’s do it! 4.1, p. 216 – What Type of Variable? • Think about it, p. 217.

  11. Parameters and Statistics • For quantitative variables (discrete or continuous), the most commonly used statistic is the average of the numbers. • Average weight of the postal packages. • For qualitative variables, the most commonly used statistic is the proportion of values in a specific category. • Proportion of packages that are in the light category.

  12. Qualitative or Quantitative? • Caution: Sometimes numbers are used merely as labels on the categories. That alone will not make the data quantitative.

  13. Qualitative or Quantitative? • On an opinion survey: • 1 = strongly disagree • 2 = disagree • 3 = neutral • 4 = agree • 5 = strongly agree • Is it legitimate to average the responses?

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