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# Graphic Presentation Of Data

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• ### Graphic Presentation Of Data

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