1. Graphical and Tabular Descriptive Techniques
Chapter Two
Graphical and Tabular
Descriptive Techniques

2. Introduction & Re-cap
Descriptive statistics involves arranging, summarizing, and presenting a set of data in such a way that useful information is produced to enable meaningful interpretation, and to support decision making.
Its methods make use of graphical techniques and numerical descriptive measures (such as averages) to summarize and present the data.
Data
Statistics
Information

3. Populations & Samples Population Sample
Subset
The graphical & tabular methods presented here apply to both entire populations and samples drawn from populations.

4. Definitions
A variable（變數） is some characteristic of a population or sample that is of interest for us.
E.g. student grades.
Typically denoted with a capital letter: X, Y, Z…
The values of the variable are the range of possible values for a variable.
E.g. student marks (0..100)
Data are the observed values（觀測值） of a variable.
E.g. student marks: {67, 74, 71, 83, 93, 55, 48}

5. 2.1 Types of Data & Information
Data (at least for purposes of Statistics) fall into three main groups:
Interval Data Nominal Data Ordinal Data
Person Marital status
1 married
2 single
3 single
. .
Age - income
. .
Computer Brand
1 IBM
2 Dell
3 IBM
. .
Weight gain
+10 +5 .
Knowing the type of data is necessary to properly select the technique to be used when analyzing data.

6. Interval Data（區間尺度資料）
• Real numbers, i.e. heights, weights, prices, etc.
• Also referred to as quantitative or numerical.
Arithmetic operations can be performed on Interval Data, thus its meaningful to talk about 2*Height, or Price + $1, and so on.

7. Nominal Data（名目尺度資料） Nominal Data
• The values of nominal data are categories.
E.g. responses to questions about marital status,
coded as:
Single = 1, Married = 2, Divorced = 3, Widowed = 4
Because the numbers are arbitrary arithmetic operations don’t make any sense (e.g. does Widowed ÷ 2 = Married?!)
Nominal data are also called qualitative or categorical.

8. Ordinal Data（順序尺度資料）
Ordinal Data appear to be categorical in nature, but their values have an order; a ranking to them:
E.g. College course rating system:
poor = 1, fair = 2, good = 3, very good = 4, excellent = 5
While its still not meaningful to do arithmetic on this data (e.g. does 2*fair = very good?!), we can say things like:
excellent > poor or fair < very good
That is, order is maintained no matter what numeric values are assigned to each category.

9. Types of Data & Information
Categorical?
Data
Interval Data
Nominal Data
Ordinal Data N
Ordered? Y
Categorical Data

10. E.g. Representing Student Grades
Data
Categorical?
Interval Data
e.g. {0..100}
Nominal Data
e.g. {Pass | Fail}
Ordinal Data
e.g. {F, D, C, B, A} N
Ordered? Y
Categorical Data
Rank order to data
NO rank order to data

11. Calculations for Types of Data
As mentioned above,
• All calculations are permitted on interval data.
• Only calculations involving a ranking process are allowed for ordinal data.
• No calculations are allowed for nominal data, save counting the number of observations in each category.
This lends itself to the following “hierarchy of data”.

12. Hierarchy of Data Interval Values are real numbers.
All calculations are valid.
Data may be treated as ordinal or nominal.
Ordinal
Values must represent the ranked order of the data.
Calculations based on an ordering process are valid.
Data may be treated as nominal but not as interval.
Nominal
Values are the arbitrary numbers that represent categories.
Only calculations based on the frequencies of occurrence are valid.
Data may not be treated as ordinal or interval.

13. 2.2 Graphical & Tabular Techniques for Nominal Data
The only allowable calculation on nominal data is to count the frequency of each value of the variable.
We can summarize the data in a table that presents the categories and their counts called a frequency distribution（次數分配）.
A relative frequency distribution（相對次數分配） lists the categories and the proportion with which each occurs.
Refer to Example 2.1

14. Nominal Data (Tabular Summary)
Table 2.1
Frequency and Relative Frequency Distributions for Example 2.1

15. Figure 2.1 Bar Chart for Example 2.1

16. Figure 2.2 Pie Chart for Example 2.1

17. Nominal Data－Bar Chart（長條圖）
Rectangles represent each category.
The height of the rectangle represents the frequency.
The base of the rectangle is arbitrary

18. Nominal Data－Pie Chart（圓餅圖）
The pie chart is a circle, subdivided into a number of slices that represent the various categories.
The size of each slice is proportional to the percentage corresponding to the category it represents.
Pie Charts show relative frequencies.

19. Table 2.2 Proportion in Each Category in Example 2.1

20. Nominal Data It all the same information, (based on the same data).
Just different presentation.

21. Table 2.3 Daily Oil Production

22. Figure 2.3 Bar Chart for Example 2.2

23. Table 2.4 Percent Share of Television Viewers

24. Figure 2.4 Pie Chart for Example 2.3

25. 2.3 Graphical Techniques for Interval Data
There are several graphical methods that are used when the data are interval (i.e. numeric, non-categorical).
The most important of these graphical methods is the histogram（直方圖）.
The histogram is not only a powerful graphical technique used to summarize interval data, but it is also used to help explain probabilities.

26. Building a Histogram Collect the Data
Example 2.4: Providing information concerning the monthly bills of new subscribers in the first month after signing on with a telephone company.
Collect data
(There are 200 data points

27. Building a Histogram Collect the Data 
Create a frequency distribution for the data…
How?
a) Determine the number of classes（組數） to use…
Refer to Table 2.6:
With 200 observations, we should have between 7 & 10 classes…
Alternative, we could use Sturges’ formula: Number of class intervals = log (n)

28. Building a Histogram Collect the Data 
Create a frequency distribution for the data…
How?
a) Determine the number of classes to use. [8] 
b) Determine how large to make each class…
Look at the range（全距） of the data, that is,
Range = Largest Observation – Smallest Observation
Range = $ – $0 = $119.63
Then each class width（組寬） becomes:
Range ÷ (# classes) = ÷ 8 ≈ 15

29. Class width
It is generally best to use equal class width, but sometimes unequal class width are called for.
Unequal class width is used when the frequency associated with some classes is too low.
Then,several classes are combined together to form a wider and “more populated” class.
It is possible to form an open ended class at the higher end or lower end of the histogram.

30. Building a Histogram Collect the Data 
Create a frequency distribution for the data…
How?
a) Determine the number of classes to use. [8] 
b) Determine how large to make each class. [15] 
c) Place the data into each class…
each item can only belong to one class;
classes contain observations greater than or equal to their lower limits and less than their upper limits.
i.e. lower limit ≦ x ＜ upper limit

31. Building a Histogram Collect the Data 
Create a frequency distribution for the data. 
3) Draw the Histogram…

32. Building a Histogram Collect the Data 
Create a frequency distribution for the data. 
Draw the Histogram. 

33. Interpret (18+28+14=60)÷200 = 30% about half (71+37=108)
of the bills are “small”,
i.e. less than $30
There are only a few telephone
bills in the middle range.
( =60)÷200 = 30%
i.e. nearly a third of the phone bills
are $90 or more.

34. Relative frequency（相對次數）
It is often preferable to show the relative frequency (proportion) of observations falling into each class, rather than the frequency itself.
Relative frequencies should be used when
the population relative frequencies are studied
comparing two or more histograms
the number of observations of the samples studied are different
Class relative frequency =
Class frequency
Total number of observations

35. Shapes of Histograms Symmetry（對稱性）
A histogram is said to be symmetric if, when we draw a vertical line down the center of the histogram, the two sides are identical in shape and size:
Frequency
Variable

36. Shapes of Histograms Skewness（偏態）
A skewed histogram is one with a long tail extending to either the right or the left:
Frequency
Variable
Positively Skewed
（正、右偏）
Negatively Skewed
（負、左偏）

37. Shapes of Histograms Modality（眾數組個數）
A unimodal histogram is one with a single peak, while a bimodal histogram is one with two peaks:
Frequency
Variable
Unimodal（單峰）
Bimodal（雙峰）
A modal class is the class with
the largest number of observations

38. Shapes of Histograms Bell Shape（鐘形分配）
A special type of symmetric unimodal histogram is one that is bell shaped:
Frequency
Variable
Many statistical techniques require that the population be bell shaped.
Drawing the histogram helps verify the shape of the population in question.
Bell Shaped

39. Histogram Comparison Example 2.6 & Example 2.7
Comparing students’ performance.
The two classes differed in their teaching emphasis
Class A – mathematical analysis and development of theory.
Class B – applications and computer based analysis.
The final mark for each student in each course was recorded.
Draw histograms and interpret the results.

40. Histogram Comparison
Compare & contrast the following histograms based on data from Example 2.6 & Example 2.7.
The two courses have very different histograms…
unimodal vs. bimodal
spread of the marks (narrower | wider)

41. Stem & Leaf Display（枝(莖)葉圖）
Retains information about individual observations that would normally be lost in the creation of a histogram.
Split each observation into two parts, a stem and a leaf:
e.g. Observation value: 42.19
There are several ways to split it up…
We could split it at the decimal point:
Or split it at the “tens” position (while rounding to the nearest integer in the “ones” position)
Stem
Leaf 42 19 4 2

42. Thus, we still have access to our original data point’s value!
Stem & Leaf Display
Continue this process for all the observations. Then, use the “stems” for the classes and each leaf becomes part of the histogram (based on Example 2.4 data) as follows…
Stem Leaf
Thus, we still have access to our original data point’s value!

43. Histogram and Stem & Leaf

44. Ogive（肩形圖） (pronounced “Oh-jive”) is a graph of
a cumulative frequency distribution（累積次數分配）.
We create an ogive in three steps…
First, from the frequency distribution created earlier, calculate relative frequencies（相對次數）:
Relative Frequency = # of observations in a class
Total # of observations

45. Relative Frequencies
For example, we had 71 observations in our first class (telephone bills from $0.00 to $15.00). Thus, the relative frequency for this class is 71 ÷ 200 (the total # of phone bills) = (or 35.5%)

46. Ogive Is a graph of a cumulative frequency distribution.
We create an ogive in three steps…
1) Calculate relative frequencies. 
2) Calculate cumulative relative frequencies（累積相對次數） by adding the current class’ relative frequency to the previous class’ cumulative relative frequency.
(For the first class, its cumulative relative frequency is just its relative frequency)

47. Cumulative Relative Frequencies
first class…
next class: =.540 :
last class: =1.00

48. Ogive Is a graph of a cumulative frequency distribution.
1) Calculate relative frequencies. 
2) Calculate cumulative relative frequencies. 
3) Graph the cumulative relative frequencies…

49. Ogive The ogive can be used to answer questions like:
What telephone bill value is at the 50th percentile?
“around $35”

50. 2.4 Two Nominal Variables
So far we’ve looked at tabular and graphical techniques for one variable (either nominal or interval data).
A contingency table（列聯表） (also called a cross-classification table or cross-tabulation table（交叉分類表）) is used to describe the relationship between two nominal variables.
A contingency table lists the frequency of each combination of the values of the two variables.

51. Contingency Table
In Example 2.8, a sample of newspaper readers was asked to report which newspaper they read: Globe and Mail (1), Post (2), Star (3), or Sun (4), and to indicate whether they were blue-collar worker (1), white-collar worker (2), or professional (3).
This reader’s response is captured as part of the total number on the contingency table…

52. Contingency Table similar dissimilar
Interpretation: The relative frequencies in the columns 2 & 3 are similar, but there are large differences between columns 1 and 2 and between columns 1 and 3.
This tells us that blue collar workers tend to read different newspapers from both white collar workers and professionals and that white collar and professionals are quite similar in their newspaper choice.
dissimilar
similar

53. Graphing the Relationship Between Two Nominal Variables
Use the data from the contingency table to create bar charts…
Professionals tend to read the Globe & Mail more than twice as often as the Star or Sun…

54. Graphing the Relationship Between Two Interval Variables
Moving from nominal data to interval data, we are frequently interested in how two interval variables are related.
To explore this relationship, we employ a scatter diagram（散佈圖）, which plots two variables against one another.
The independent variable（自變數） is labeled X and is usually placed on the horizontal axis, while the other, dependent variable（因變數）, Y, is mapped to the vertical axis.

55. Scatter Diagram
Example 2.9 A real estate agent wanted to know to what extent the selling price of a home is related to its size…
Collect the data 
Determine the independent variable (X – house size) and the dependent variable (Y – selling price) 
Use Excel to create a “scatter diagram”…

56. Scatter Diagram
It appears that in fact there is a relationship, that is, the greater the house size the greater the selling price…

57. Patterns of Scatter Diagrams
Linearity（線性相關） and Direction are two concepts we are interested in.
Positive Linear Relationship
Negative Linear Relationship
Weak or Non-Linear Relationship

58. 2.5 Time Series Data
Observations measured at the same point in time are called cross-sectional data（橫斷面資料）.
Marketing survey (observe preferences by gender, age)
Test score in a statistics course
Starting salaries of an MBA program graduates
Observations measured at successive points in time are called time-series data（時間序列資料）.
Weekly closing price of gold
Amount of crude oil imported monthly
Time-series data graphed on a line chart（線圖）, which plots the value of the variable on the vertical axis against the time periods on the horizontal axis.

59. Line Chart
From Example 2.10, plot the total amounts of U.S. income tax for the years 1987 to 2002.

60. Line Chart
From ’87 to ’92, the tax was fairly flat. Starting ’93, there was a rapid increase taxes until Finally, there was a downturn in 2002.

61. Summary I
Factors That Identify When to Use Frequency and Relative Frequency Tables, Bar and Pie Charts
1. Objective: Describe a single set of data.
2. Data type: Nominal
Factors That Identify When to Use a Histogram, Ogive, or Stem-and-Leaf Display
2. Data type: Interval
Factors that Identify When to Use a Contingency Table
1. Objective: Describe the relationship between two variables.
Factors that Identify When to Use a Scatter Diagram

62. Summary II Interval Data Nominal Single Set of Data Histogram, Ogive,
Stem-and-Leaf Display
Frequency and Relative Frequency Tables,
Bar and Pie Charts
Relationship Between
Two Variables
Scatter Diagram
Contingency Table,
Bar Charts