1. Principles of Graphical Excellence
Department Of Statistics
University of Peshawar
Principles of Graphical Excellence

2. The Visual Display of Quantitative Information

3. History of Graphical Development
17th Century: combined map skills and statistical skills to construct maps.
Trade winds and monsoons on a world map.
Chart patterns of disease.
Later sophistication showed distribution of 1.3 million galaxies.
First geographic maps were drawn on clay tablets.
The 17th century combined map skills and statistical skills to construct maps.
One of the first data maps was Halley’s 1686 chart showing trade winds and monsoons
on a world map.
Early use was to chart patterns of disease. Dr. John Snow plotted deaths from cholera in central London for September He found the most deaths at eleven water pumps, and then had those water pump handles removed, which had taken over 500 people.
Later sophistical showed distribution of 1.3 million galaxies, but the producers were cautiuous to observe that the visual impression may be misleading: there is no linearity patterns in random noise.

4. Introduction
Whenever verbal problems involving a certain situation is presented visually before the learners, it makes easier for the learner to understand the problem and attempt its solution.
Similarly, when the data are presented pictorially (or graphically) before the learners, it makes the presentation eye-catching and more intelligible. The learners can easily see the salient features of the data and interpret them.

5. Continued.. He invented three of the four basic forms of graph:
The statistical line graph
The bar graph
The pie graph

6. “Graphical excellence consists of the efficient communication of complex quantitative ideas.”

7. Presentation Topics Organizing Numerical Data:
The Ordered Array and Stem-leaf Display
Tabulating and Graphing Numerical Data:
Frequency Distributions: Tables, Histograms, Polygons
Cumulative Distributions: Tables, the Ogive

8. Presentation Topics (continued)
Tabulating and Graphing Univariate Categorical Data:
The Summary Table
Bar and Pie Charts, the Pareto Diagram
Tabulating and Graphing Bivariate Categorical Data:
Contingency Tables
Side by Side Bar charts
Graphical Excellence and Common Errors in Presenting Data

9. “At their best, graphics are instruments for reasoning about quantitative information.”

10. Organizing Numerical Data
41, 24, 32, 26, 27, 27, 30, 24, 38, 21
Frequency Distributions
Cumulative Distributions
Ordered Array
21, 24, 24, 26, 27, 27, 30, 32, 38, 41
3 028
4 1
Ogive
Histograms
Stem and Leaf
Display
Tables
Polygons

11. Organizing Numerical Data:
Data in Raw form (as collected):
24, 26, 24, 21, 27, 27, 30, 41, 32, 38
Date Ordered from Smallest to Largest:
21, 24, 24, 26, 27, 27, 30, 32, 38, 41
Stem and Leaf display:
4 1

12. “Design is choice.”

13. Tabulating and Graphing Numerical Data
41, 24, 32, 26, 27, 27, 30, 24, 38, 21
Frequency Distributions
Cumulative Distributions
Ordered Array
21, 24, 24, 26, 27, 27, 30, 32, 38, 41
4 1
Ogive
Histograms
Stem and Leaf
Display
Tables
Polygons

14. Tabulating Numerical Data: Frequency Distributions
(continued)
Data in ordered array:
12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58
Relative
Frequency
Percentage
Class Frequency
10 but under
20 but under
30 but under
40 but under
50 but under
Total

15. Graphing Numerical Data: The Histogram
Data in ordered array:
12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58
No Gaps Between Bars
Class Midpoints

16. Graphing Numerical Data:
The Frequency Polygon
Data in ordered array:
12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58
Class Midpoints

17. Tabulating Numerical Data:
Cumulative Frequency
Data in ordered array:
12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58
Cumulative Cumulative
Class Frequency % Frequency
10 but under
20 but under
30 but under
40 but under
50 but under

18. Graphing Numerical Data: The Ogive (Cumulative % Polygon)
Data in ordered array:
12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58

19. Tabulating and Graphing Categorical Data: Univariate Data
Graphing Data
Tabulating Data
The Summary Table
Pie Charts
Pareto Diagram
Bar Charts

20. (University Revenues)
Summary Table
(University Revenues)
Revenue Category Amount Percentage
(in thousands $)
Patient Services
Tuition/fees
Appropriations
Grants/Contracts
Total
Variables are Categorical.

21. Graphing Categorical Data: Univariate Data
Graphing Data
Tabulating Data
The Summary Table
Pie Charts
Bar Charts

22. Types of Graphs
Bar graphs Pie graphs

23. Bar graphs
It is also called a columnar diagram. The bar diagrams are drawn through columns of equal width. Following rules were observed while constructing a bar diagram:
(a) The width of all the bars or columns is similar.
(b) All the bars are placed on equal intervals/distance.
(c) Bars are shaded with colours or patterns to make them distinct and attractive.

24. The simple bar diagram
A simple bar diagram is constructed for an immediate comparison. It is advisable to arrange the given data set in an ascending or descending order and plot the data variables accordingly. However, time series data are represented according to the sequencing of the time period.

25. The simple bar diagram

26. Pie graphs
Pie diagram is another graphical method of the representation of data. It is drawn to depict the total value of the given attribute using a circle. Dividing the circle into corresponding degrees of angle then represent the sub– sets of the data. Hence, it is also called as Divided Circle Diagram.

27. Students by Classification
Pie Chart
(for a factbook)
Students by Classification
MSc 15%
FSc 42%
BSc 14%
Percentages are rounded to the nearest percent.
PhD 29%

28. Tabulating and Graphing Bivariate Categorical Data
Contingency Tables
Side by Side Charts

29. Compound bar diagram
When different components are grouped in one set of variable or different variables of one component are put together, their representation is made by a compound bar diagram. In this method, different variables are shown in a single bar with different rectangles.

30. Graphing Categorical Data: Bivariate Data
Side by Side
Chart

31. Conclusion
Graphical form makes it possible to easily draw visual impressions of data.
The graphic method of the representation of data enhances our understanding.
It makes the comparisons easy.
Besides, such methods create an imprint on mind for a longer time

32. Principles of Graphical Excellence
Well designed presentation of data that provides:
Substance
Statistics
Design
Communicates complex ideas with clarity, precision and efficiency
Gives the largest number of ideas in the most efficient manner
Almost always involves several dimensions
Requires telling the truth about the data

33. ‘Chart Junk’  Bad Presentation Good Presentation $ Minimum Wage
1960: $1.00 4
1970: $1.60 2
In time-series displays of money, either present the graphics in proportion to the change, or show nominal units.
1980: $3.10
1990: $3.80
1960
1970
1980
1990

34. No Zero Point on Vertical Axis
Bad Presentation
Good Presentation
Monthly Expenses
Monthly Expenses $ $ 45 60 42 40 39 20
Always show zero on your chart, then indicate the level of beginning measurement. 36 J F M A M J J F M A M J
Graphing the first six months of sales.

35. Presentation Summary Organized Numerical Data:
The Ordered Array and Stem-leaf Display
Tabulated and Graphed Numerical Data
Frequency Distributions: Tables, Histograms, Polygons
Cumulative Distributions: Tables, the Ogive

36. Presentation Summary (continued)
Tabulated and Graphed Univariate Categorical Data:
The Summary Table
Bar and Pie Charts, the Pareto diagram
Tabulated and Graphed Bivariate Categorical Data:
Contingency Tables
Side by Side charts
Discussed Graphical Excellence and Common Errors in Presenting Data