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Chapter 10

Chapter 10. STA 200 Summer I 2011. Data Tables. One way to organize data is to create a data table. Good data tables have: a clear heading clearly labeled variables rates instead of (or supplemental to) counts. Example. 1912 Presidential Election:. Data Tables (cont.).

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Chapter 10

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  1. Chapter 10 STA 200 Summer I 2011

  2. Data Tables • One way to organize data is to create a data table. • Good data tables have: • a clear heading • clearly labeled variables • rates instead of (or supplemental to) counts

  3. Example 1912 Presidential Election:

  4. Data Tables (cont.) • A data table shows: • what values a variable takes • how often it takes those values • In other words, a data table shows the distribution of a variable.

  5. Types of Variables • Some variables place individuals into categories (gender, eye color, state of residence), while others have a meaningful numerical scale (distance, height, exam score). • A variable that places individuals into categories is called a categorical variable. • A variable with a meaningful numerical scale is called a quantitative variable.

  6. Graphing Categorical Variables • Categorical variables can be graphed using a pie chart or a bar graph. • Thus, we could graph the presidential election data using either a pie chart or bar graph. • When constructing a bar graph, make sure the bars don’t touch.

  7. Example (Pie Chart)

  8. Example (Bar Graph)

  9. or…

  10. Bad Graphs (Pictogram)

  11. Pictograms • A pictogram is another way to graphically display a categorical variable. • Pictograms can be misleading, since the difference between two values is often poorly represented.

  12. Bad Graphs (Clutter)

  13. Line Graphs • Line graphs are used to display change over time. • When constructing a line graph, always put time on the horizontal axis and the variable on the vertical axis.

  14. Example

  15. Line Graphs (cont.) • Things to look for: • patterns/trends • deviations • seasonal variation • Seasonal variation occurs if a pattern in a graph repeats over regular time intervals.

  16. Example (Seasonal Variation)

  17. Misrepresenting Data • It’s easy for data to be misrepresented in a line graph. • Depending on how the axes are calibrated, change over time can look more severe or less severe than it should.

  18. Example (Misrepresenting Data)

  19. Example (cont.)

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