1. CS 235: User Interface Design November 26 Class Meeting Department of Computer Science San Jose State University Fall 2014 Instructor: Ron Mak www.cs.sjsu.edu/~mak

2. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Online Course Evaluations  Evaluation period closes Wednesday, Dec. 10.  If you don’t fill out the online SOTES by Dec. 10, you will have a 3-week delay in the release of your grades. 2

3. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Examples of Quantitative Relationships Quantitative informationRelationship Units of a product sold per geographic region Sales related to geography Revenue by quarterRevenue related to time Expenses by department and monthExpenses related to organizational structure and time A company’s market share compared to that of its competitors Market share related to companies The number of employees who received each of five possible performance ratings (1-5) during the last annual performance review Employee counts related to performance ratings 3 Show Me the Numbers, 2 nd ed. by Stephen Few Analytics Press, 2012

4. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Relationships within Categories  Nominal  Ordinal  Interval  Hierarchical 4

5. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Relationships within Categories: Nominal  Values in a category are discrete and have no intrinsic order.  Example: 5 Show Me the Numbers, 2 nd ed. by Stephen Few Analytics Press, 2012

6. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Relationships within Categories: Ordinal  Categorical items have a prescribed order. Meaningless to display them out of order. Except perhaps in reversed order.  Examples: First, second, third, … Small, medium, large Best, second best, … 6

7. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Relationships within Categories: Interval  Categorical items consist of a sequential series of numerical ranges that subdivide a larger range of quantitative values into smaller ranges.  Examples: 7 Show Me the Numbers, 2 nd ed. by Stephen Few Analytics Press, 2012

8. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Relationships within Categories: Hierarchical  Multiple categories that are closely associated with each other as separate levels in a series of parent-child connections.  Example: 8 Show Me the Numbers, 2 nd ed. by Stephen Few Analytics Press, 2012

9. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Relationships between Quantities  Ranking  Ratio  Correlation 9

10. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Relationships between Quantities: Ranking  The order in which categorical items are displayed is based on associated quantitative values.  Example: 10 Show Me the Numbers, 2 nd ed. by Stephen Few Analytics Press, 2012

11. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Relationships between Quantities: Ratio  A number that expresses the relative quantities of two values obtained by dividing one by the other.  The ratio of a part to the whole is generally expressed as a percentage.  Example: 11 Show Me the Numbers, 2 nd ed. by Stephen Few Analytics Press, 2012

12. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Relationships between Quantities: Correlation  A comparison of two paired sets of quantitative values to determine whether increases in one value correspond to either increases or decreases in the other.  Allows us to predict the values of one variable by knowing or controlling the values of another.  Example: Number of years employees have been doing particular jobs vs. productivity in those jobs. Does productivity increase or decrease with tenure? 12

13. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Numbers that Summarize  Measures of average Mean Median  Measures of variation Spread Standard deviation  Measures of ratio  Measures of correlation Linear correlation coefficient 13

14. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Measures of Average: Arithmetic Mean  Sum all the values and divide the sum by the number of values.  Treats every value equally, no matter now extreme.  Example: 14 Show Me the Numbers, 2 nd ed. by Stephen Few Analytics Press, 2012

15. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Measures of Average: Arithmetic Mean, cont’d  The arithmetic mean can be a misleading summary of a set of numbers.  Example: 15 Show Me the Numbers, 2 nd ed. by Stephen Few Analytics Press, 2012

16. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Measures of Average: Median  The middle value of a sorted set of values.  Not sensitive to extreme values.  Better at expressing what’s a typical value.  Example: 16 Show Me the Numbers, 2 nd ed. by Stephen Few Analytics Press, 2012

17. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Measures of Variation: Spread  The difference between the lowest and the highest of a set of values. 17 Show Me the Numbers, 2 nd ed. by Stephen Few Analytics Press, 2012

18. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Measures of Variation: Standard Deviation  Measures the variation in a set of values relative to the arithmetic mean.  Formulas: 18 For n items taken from the entire set. For the entire set of n items.

19. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Measures of Variation: Standard Deviation, cont’d  Percentage of values that fall within 1, 2, or 3 standard deviations from the mean in a normal distribution. 19 68% of the values 95% of the values 99.7% of the values Show Me the Numbers, 2 nd ed. by Stephen Few Analytics Press, 2012

20. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Ways to Express Measures of Ratio  Sentence Example: Two out of five customers …  Fraction Examples: ½ ⅔ ⅞  Rate Example: 0.4  Percentage Example: 45% 20

21. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Types of Analyses  Time series  Part-to-Whole and Ranking  Deviation  Distribution  Correlation  Multivariate 21 Ultimate goal: Provide insight for the user.

22. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Correlation Analysis  Characteristics of correlation: Direction Strength Shape 22

23. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Linear Correlation Coefficient  All values are between -1 and +1.  0 = no correlation.  +1 = perfect positive linear correlation  -1 = perfect negative linear correlation  The closer the value is to -1 or +1, the stronger the linear correlation. 23

24. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Linear Correlation Coefficient, cont’d 24 Show Me the Numbers, 2 nd ed. by Stephen Few Analytics Press, 2012

25. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Linear Correlation Coefficient, cont’d 25 Show Me the Numbers, 2 nd ed. by Stephen Few Analytics Press, 2012

26. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Linear Correlation Coefficient, cont’d 26 Show Me the Numbers, 2 nd ed. by Stephen Few Analytics Press, 2012

27. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Logarithmic Correlation 27 Now You See It by Stephen Few Analytics Press, 2009

28. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Exponential Correlation 28 Now You See It by Stephen Few Analytics Press, 2009

29. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Polynomial Correlation 29 Show Me the Numbers, 2 nd ed. by Stephen Few Analytics Press, 2012

30. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Correlation Does Not Imply Causation!  A strong correlation between two variables does not imply that one causes the other.  Further statistical tests are necessary to calculate the likelihood of true causation.  See http://en.wikipedia.org/wiki/Correlation_does_not_imply _causation http://en.wikipedia.org/wiki/Correlation_does_not_imply _causation 30

31. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Correlation Does Not Imply Causation! cont’d  See http://en.wikipedia.org/wiki/Correlation_does_not_imply _causation http://en.wikipedia.org/wiki/Correlation_does_not_imply _causation 31 Invalid insight: Eating ice cream causes crime. (Actually, both are caused by warmer weather.)

32. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Correlation Does Not Imply Causation! cont’d  Highly correlated: Infants sleeping with the lights on and the development of myopia (near sightedness).  Invalid insight: Sleeping with the lights on causes a child to become near-sighted.  True causation: Parents who are near-sighted have more lights on. Children inherit near-sightedness from their parents. 32

33. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Beware of False Correlations!  See: http://www.tylervigen.com http://www.dailymail.co.uk/sciencetech/article- 2640550/Does-sour-cream-cause-bike-accidents- No-looks-like-does-Graphs-reveal-statistics- produce-false-connections.html http://www.dailymail.co.uk/sciencetech/article- 2640550/Does-sour-cream-cause-bike-accidents- No-looks-like-does-Graphs-reveal-statistics- produce-false-connections.html http://www.buzzfeed.com/kjh2110/the-10-most- bizarre-correlations http://www.buzzfeed.com/kjh2110/the-10-most- bizarre-correlations 33

34. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Multivariate Analysis  Compare multiple instances of several variables at once.  Identify similarities and differences among items that are each characterized by a common set of variables. Which items are most alike? Which items are most exceptional? How can items be grouped based on similarity? What multivariate profile corresponds best to a particular outcome? 34

35. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Multivariate Displays  Glyphs  Whiskers and stars  Multivariate heatmaps  Parallel coordinate plots 35

36. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Multivariate Displays: Glyphs VariableVisual attribute Body temperatureColor Blood typeHead shape Body mass indexTorso thickness Heart ratePosition of the arms Blood sugar levelPosition of the legs 36 Now You See It by Stephen Few Analytics Press, 2009

37. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Multivariate Displays: Glyphs, cont’d  Chernoff faces 37 http://mathworld.wolfram.com/ChernoffFace.html

38. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Multivariate Displays: Whiskers and Stars  Each line represents a different variable.  The line length encodes the variable’s value. 38 Now You See It by Stephen Few Analytics Press, 2009

39. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Multivariate Displays: Heatmaps 39 Now You See It by Stephen Few Analytics Press, 2009

40. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Multivariate Displays: Parallel Plots 40 Now You See It by Stephen Few Analytics Press, 2009

41. Computer Science Dept. Fall 2014: November 26 CS 235: User Interface Design © R. Mak Multivariate Displays: Parallel Plots, cont’d  Look for patterns! 41 Now You See It by Stephen Few Analytics Press, 2009