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Graphic Presentation Of Data

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  • Transcript

    • 1. Graphic Presentation of Data
    • 2. Purpose of a Graph
      • A visual presentation of data
        • Relationships & comparisons are visual
        • Not as daunting as a table of numbers
        • Allows some artistry and creativity
      • Accuracy is important
      • Style of graph must match
        • Level of Measurement of the variable(s)
        • Nature of this particular data set
    • 3. Graphs for Discrete Data
      • Data are in categories
        • Nominal
        • Ordinal if the number of categories is small
      • Types of graph:
        • Pie Chart
        • Bar Chart
      • Graph shows the distribution of cases (scores) per category
      • Label the categories
      • Show the Frequency (count) or Percent
    • 4. Pie Chart
      • Area of pie = 100%
      • Each wedge is in proportion to the percentage for that category
      • Labels show count or percent
      • Ten slices is about the most to use
    • 5. Pie chart methods can also obscure the pattern in the data
    • 6. Many problems in this graph!
    • 7. Bar Chart
      • Area of bars combined is 100%
      • Area of each bar is proportional to its percent of total
      • Bars do not touch because categories are discrete.
      • Many variations; this is the most simple.
    • 8. Some graphic methods obscure the trends in the data: BAD IDEA!
    • 9. Pictograph: equal size pictures not a problem
    • 10. Unequal pictures make for an inaccurate graph
    • 11. Which tree does your eye go to first? Which is most prominent? Which has highest count?
    • 12. What problems can you identify in this graph?
    • 13. Graphs for Continuous Data (Sometimes Ordinal)
      • Horizontal axis goes from low to high
        • Intervals shown for Interval or Ratio data
        • Some ordinal data also graphed this way (e.g., strongly agree, agree, slightly agree, etc)
      • Graph shows continuity of the construct
        • Histogram: bars that touch at real limits
        • Line graph: covers range (a.k.a. Frequency Polygon)
    • 14. Histogram
      • Ranges of scores grouped together
      • Ranges equal width whenever possible
      • Labels show mid-point or real limits
      • Low scores on left, high scores on right
    • 15. What’s wrong with this histogram?
    • 16. Check all the details!
    • 17. Line Graphs / Frequency Polygon
      • Horizontal axis goes from low to high
      • Line shows level for each value
        • Frequency count
        • Percentage
        • (Sometimes) average
      • Only suitable for Interval or Ratio data
    • 18. Editorializing the Data is improper
    • 19. Using 3D multiplies the impact and leads to inaccurate interpretation
    • 20. Which problem(s) are here?
    • 21. Where is the 0% point on this graph? Is work increasing or decreasing? What do the lines represent? What do the people and the bus add to our knowledge?
    • 22. Why doesn’t this graph look like GROWTH? (Hint: check out the axes)
    • 23. Population Map outside T 3649 Area=population, displayed by location. Great!
    • 24. Any questions?
    • 25. Keep your eyes open for bad graphs. They’re everywhere!! (You may find some excellent ones too...) For next week: Chapter 3 on Summarizing data Central Tendency (Averages)

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