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: CandidatePartyNumber of Votes Percentage W. WilsonDemocratic 6,296,28441.8% T. RooseveltProgressive 4,122,72127.4% W. H. TaftRepublican 3,486,24223.2% E. V. DebsSocialist 901,5516.0% Other242,0361.6% Total15,048,834100.0%

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.)