week 2 slides_15 (finance1)
TRANSCRIPT
BUSS1020 1
WEEK 2: ORGANIZING AND VISUALIZING DATA
BUSS1020 Quantitative Business Analysis
BUSS1020 2http://visual.ly/nachos-101
http://visual.ly/power-infographics
BUSS1020
BUSS1020 4
LEARNING OBJECTIVES
In this section you will learn: To construct tables and charts to visualize and organise numerical data
To construct tables and charts to visualize and organise categorical data
The principles of properly presenting summarized data and graphs
Text pages 37-107
Focus is on structured data
BUSS1020 5
AGENDA
Introduction
Categorical Data Organising One Variable Categorical Data Visualising One Variable Categorical Data Organising Two Variable Categorical Data Visualising Two Variable Categorical Data
Numerical Data Organising Numerical Data Visualising One Variable Numerical Data Visualising Two Variable Numerical Data
Principles of Graphical Excellence
DCOVADCOVADCOVADCOVA
DCOVADCOVADCOVA
DCOVA
6BUSS1020
BUSS1020 7
Data is organised and visualized so as to
reveal, glean insight from and communicate
the information,
especially the main features and patterns,
that are hidden within it.
First step: find the “story” in the data
Last step: communicate the “story”, well
ORGANISING AND VISUALISING DATA
ORGANIZING CATEGORICAL DATA: TABLES
BUSS1020 8
Categorical Data
Tallying Data
Summary Table
One Categorical
Variable
Two Categorical Variables
Contingency Table
DCOVA
BUSS1020
SUMMARY TABLE
Banking Preference? PercentATM 16%
Telephone 2%
Drive-through service at branch 17%
In person at branch 41%
Internet 24%
9
A summary table indicates the frequency, amount, percentage or proportion of items in each of a set of categories
This allows you to see: the relative frequency of each category the differences between the categories
A Survey of 1000 Bank Customers:
Should the bank stop telephone banking?
BUSS1020 10
FIDELITY INVESTMENTS
Once considered stopping its bill-paying service for customers
The service was losing money and used by very few customers
However, an analysis of their customer database showed that those using this service were among the most loyal and the most profitable customers.
Fidelity retained the service, so as not to lose those customers who contributed enormously to their profit margin.
11BUSS1020
BUSS1020 12
Frequency table results for Type: Count = 316
Summary table for Retirement Fund data: “Type”
Type Frequency Percent of Total
Growth 227 71.8
Value 89 28.2
Retirement Funds.xlsx
SUMMARY TABLE
13
Risk Frequency Percent of Total
Cumulative Percent of
TotalLow 212 67.1 67.1Average 91 28.8 95.9High 13 4.1 100
Frequency table results for Risk: Count = 316
What type and scale is “Risk”?
Why did I add a Cumulative column?
Retirement Funds.xlsx
SUMMARY TABLE
BUSS1020
BUSS1020 14
AGENDA
Introduction
Categorical Data Organising One Variable Categorical DataVisualising One Variable Categorical Data Organising Two Variable Categorical Data Visualising Two Variable Categorical Data
Numerical Data Organising Numerical Data Visualising One Variable Numerical Data Visualising Two Variable Numerical Data
Principles of Graphical Excellence
DCOVADCOVADCOVADCOVA
DCOVADCOVADCOVA
DCOVA
VISUALIZING CATEGORICAL DATA (1 VAR)
BUSS1020 15
Categorical Data
Visualizing Data
BarChart
Summary Table For
One Variable
Contingency Table For Two
Variables
Side-By-Side Bar Chart
Pie Chart
ParetoChart
DCOVA
Chap 2, section 3, pg 53
BUSS1020 16
Banking Preference
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
ATM
Automated or live telephone
Drive-through service at branch
In person at branch
Internet
VISUALIZING CATEGORICAL DATA: THE BAR CHART
Banking Preference?
%
ATM 16%
Automated or live telephone
2%
Drive-through service at branch
17%
In person at branch
41%
Internet 24%
One bar for each category, bar length represents the amount, frequency or percentage of values in that category
17
Frequency of Risk level
Percentage of Fund TypeRetirement Fund data
Retirement Funds.xlsx
BUSS1020
BUSS1020
VISUALIZING CATEGORICAL DATA: THE PIE CHART
A shaded circle with one slice for each category. Slice size represents the percentage in each category.
Banking Preference
16%
2%
17%
41%
24%
ATM
Automated or livetelephone
Drive-through service atbranch
In person at branch
Internet
Banking Preference? %
ATM 16%
Automated or live telephone
2%
Drive-through service at branch
17%
In person at branch 41%
Internet 24%
18
Chap 2 pg 54
19
Frequency of Risk level
Percentages of Risk levelRetirement Fund data
Retirement Funds.xlsx
BUSS1020
BUSS1020 20
Chap 2, section 3, pg 55VISUALIZING CATEGORICAL DATA:THE PARETO CHARTFor categorical, nominal scale data
Vertical bar chart, categories shown in descending order of frequency
A cumulative polygon is also shown
Separates the “vital few” from the “trivial many”
Not available in STATCRUNCH!
See pg 92 Berenson for Excel instructions
BUSS1020
Chap 2 pg 57
THE PARETO CHART
21
War
ped
card
jam
med
Card
unr
eada
ble
ATM m
alfu
nctio
ns
ATM o
ut o
f cas
h
Inva
lid a
mou
nt re
ques
ted
Wro
ng k
eyst
roke
Lack
of f
unds
in a
ccou
nt0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Incomplete ATM transactions
BUSS1020 22
AGENDA
Introduction
Categorical Data Organising One Variable Categorical Data Visualising One Variable Categorical DataOrganising Two Variable Categorical Data Visualising Two Variable Categorical Data
Numerical Data Organising Numerical Data Visualising One Variable Numerical Data Visualising Two Variable Numerical Data
Principles of Graphical Excellence
DCOVADCOVADCOVADCOVA
DCOVADCOVADCOVA
DCOVA
23
ORGANIZING CATEGORICAL DATA: TABLES
Categorical Data
Tallying Data
Summary Table
One Categorical
Variable
Two Categorical Variables
Contingency Table
BUSS1020
DCOVA
BUSS1020 24
CONTINGENCY TABLE
Can show pattern or relationship between two or more categorical variables
Cross tabulates or tallies jointly the responses of the categorical variables
DCOVA
BUSS1020 25
Average High Low TotalGrowth 74 10 143 227Value 17 3 69 89Total 91 13 212 316
Contingency table results:Rows: TypeColumns: Risk
Contingency table for Retirement Fund data: “Type” vs “Risk”
Is there a pattern or relationship?If so, what is it?
CONTINGENCY TABLE
26
Cell format
Count(Row percent)(Column percent)
Average High Low TotalGrowthRow %
Column %
74(32.6%)
(81.32%)
10(4.41%)
(76.92%)
143(63%)
(67.45%)
227(100%)
(71.84%)
Value 17(19.1%)
(18.68%)
3(3.37%)
(23.08%)
69(77.53%)(32.55%)
89(100%)
(28.16%)Total 91
(28.8%)(100%)
13(4.11%)(100%)
212(67.09%)
(100%)
316(100%)(100%)
Contingency table results:Rows: TypeColumns: Risk
Is there a pattern or relationship?
If so, what is it?
BUSS1020
27
“PIVOT TABLE” CONTINGENCY TABLE FOR BOND DATA
Fund Number Type Assets Fees
Expense Ratio
Return 2009
3-Year Return
5-Year Return Risk
FN-1 Intermediate Government 7268.1No 0.45 6.9 6.9 5.5Below average
FN-2 Intermediate Government 475.1No 0.50 9.8 7.5 6.1Below average
FN-3 Intermediate Government 193.0No 0.71 6.3 7.0 5.6Average
FN-4 Intermediate Government 18603.5No .13 5.4 6.6 5.5Average
FN-5 Intermediate Government 142.6No 0.60 5.9 6.7 5.4Average
FN-6 Intermediate Government 1401.6No 0.54 5.7 6.4 6.2Average
BUSS1020
28
CAN EASILY CONVERT TO AN OVERALL PERCENTAGES TABLE
Intermediate government funds are much morelikely to charge a fee.
BUSS1020
BUSS1020 29
EASILY ADD VARIABLES TO AN EXISTING TABLE
Chap 2-29
Is the pattern of risk the same for all combinations offund type and fee charge?
BUSS1020 30
AGENDA
Introduction
Categorical Data Organising One Variable Categorical Data Visualising One Variable Categorical Data Organising Two Variable Categorical DataVisualising Two Variable Categorical Data
Numerical Data Organising Numerical Data Visualising One Variable Numerical Data Visualising Two Variable Numerical Data
Principles of Graphical Excellence
DCOVADCOVADCOVADCOVA
DCOVADCOVADCOVA
DCOVA
BUSS1020 31
VISUALIZING CATEGORICAL DATA (2 VAR)
Categorical Data
Visualizing Data
BarChart
Summary Table For One
Variable
Contingency Table For
Two Variables
Side-By-Side Bar Chart
Pie Chart
ParetoChart
Chap 2 Sect 3 pg 57
DCOVA
BUSS1020 32
VISUALIZING CATEGORICAL DATA:SIDE-BY-SIDE BAR CHARTSThe side by side bar chart represents the
data from a contingency table.
No Errors
Errors
0.0%
10.0
%
20.0
%
30.0
%
40.0
%
50.0
%
60.0
%
70.0
%
Invoice Size Split Out By Errors & No Errors
Large Medium Small
Invoices with errors are much more likely to be ofmedium size (61.54% vs 30.77% and 7.69%)
NoErrors Errors Total
SmallAmount
50.75% 30.77% 47.50%
MediumAmount
29.85% 61.54% 35.00%
LargeAmount
19.40% 7.69% 17.50%
Total100.0% 100.0% 100.0%
33
Risk level vs Fund Type
BUSS1020
34
Risk level vs Fund Type
BUSS1020
35
Risk level vs Fund Type
BUSS1020
36
Risk level vs Fund Type
BUSS1020
BUSS1020 37
PIE CHARTS VS BAR CHARTSWhich is best?
When should each be used?
What properties make a “good” or “bad” pie chart and/or bar chart
http://www.perceptualedge.com/examples.php
38
What issues can you see with this plot?
http://www.perceptualedge.com/example12.php
www.sharebuilder.com
BUSS1020
39
Is this better? Why?
Good points?
Negatives?
http://www.perceptualedge.com/example12.php
BUSS1020
40http://flowingdata.com/2012/06/15/what-3-d-pie-charts-are-good-for/
BUSS1020
BUSS1020 41
SOME PRINCIPLES OF GRAPHINGMaximise message; minimise noise
Include a title and label axes
Include a reference to the source
Keep things in correct proportions
Visualize This by Nathan Yau
43
Over time
Spatially
HOW DO CATEGORY PROPORTIONS CHANGE?
Interactive graphics
BUSS1020
SPATIAL “HEAT” MAPS
http://www.nytimes.com/interactive/2013/10/02/us/uninsured-americans-map.html?_r=0BUSS1020
CATEGORIES OVER TIMEhttp://www.nytimes.com/interactive/2014/08/13/upshot/where-people-in-each-state-were-born.html?abt=0002&abg=0
BUSS1020
BUSS1020 46
SOCRATIVE QUESTION ON THIS PLOT
https://b.socrative.comRoom:9633CA5F
BUSS1020 47
SOCRATIVE QUESTION ON THIS PLOT
https://b.socrative.comRoom:9633CA5F
BUSS1020 48
AGENDA
Introduction
Categorical Data Organising One Variable Categorical Data Visualising One Variable Categorical Data Organising Two Variable Categorical Data Visualising Two Variable Categorical Data
Numerical DataOrganising Numerical Data Visualising One Variable Numerical Data Visualising Two Variable Numerical Data
Principles of Graphical Excellence
DCOVADCOVADCOVADCOVA
DCOVADCOVADCOVA
DCOVA
BUSS1020 49
Chap 2 pg 44 - 51
TABLES FOR ORGANIZINGNUMERICAL DATA
Numerical Data
Ordered Array
DCOVA
CumulativeDistributions
FrequencyDistributions
BUSS1020 50
Chap 2 pg 44ORGANIZING NUMERICAL DATA: ORDERED ARRAY
Age of Surveyed Uni Students
Day Students
16 17 17 18 18 18
19 19 20 20 21 22
22 25 27 32 38 42Night Students
18 18 19 19 20 21
23 28 32 33 41 45
An ordered array is a sequence of data, in rank order, from the smallest to the largest value.
Shows range: i.e. minimum to maximum value
May help identify “outliers”
BUSS1020 51
Chap 2 pg 45
ORGANIZING NUMERICAL DATA: FREQUENCY DISTRIBUTIONA frequency distribution is a summary table: data are arranged into numerically ordered classes.
An appropriate number of class groupings, a suitable width of a class grouping, and the boundaries of each class grouping need to be chosen.
The number of class groups depends on the number of values in the data. In general, a frequency distribution should have at least 5 but no more than 15 classes.
To determine the width of a class interval, you divide the range (Highest value–Lowest value) of the data by the number of class groupings desired.
BUSS1020 52
ORGANIZING NUMERICAL DATA: FREQUENCY DISTRIBUTION EXAMPLEExample: The Bureau of Meteorology (BOM) measures the rainfall (in mm.) in July 2013 for 20 Sydney suburbs:
24, 35, 17, 21, 24, 37, 26, 46, 58, 30, 32, 13, 12, 38, 41, 43, 44, 27, 53, 27
Organize this rainfall data
BUSS1020 53
FREQUENCY DISTRIBUTION EXAMPLESort raw data in ascending order:12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58
Find range: 58 - 12 = 46
Select number of classes: 5
Compute class interval (width): 10 (46/5 then round up)
Determine class boundaries (limits): Class 1: 10 - 20 Class 2: 20 - 30 Class 3: 30 - 40 Class 4: 40 - 50 Class 5: 50 - 60
Compute class midpoints: 15, 25, 35, 45, 55
Count observations & assign to classes
54
ORGANIZING NUMERICAL DATA: FREQUENCY DISTRIBUTION EXAMPLE
Class MidpointsFrequency
10 - 20 15 3 20 - 30 25 6 30 - 40 35 5 40 - 50 45 4 50 - 60 55 2 Total 20
Data in ordered array:
12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58
BUSS1020
55
RELATIVE & PERCENT FREQUENCY DISTRIBUTION EXAMPLE
Class Frequency
10 - 20 3 .15 15 20 - 30 6 .30 30 30 - 40 5 .25 25 40 - 50 4 .20 20 50 - 60 2 .10 10 Total 20 1.00 100
RelativeFrequency
Percentage
Data in ordered array:
12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58
BUSS1020
56
ORGANIZING NUMERICAL DATA: CUMULATIVE FREQUENCY DISTRIBUTION EXAMPLE
Class
10 ≤ X< 20 3 15% 3 15%
20 ≤ X< 30 6 30% 9 45%
30 ≤ X< 40 5 25% 14 70%
40 ≤ X< 50 4 20% 18 90%
50 ≤ X< 60 2 10% 20 100%
Total 20 100 20 100%
Percentage
Cumulative
Percentage
Data in ordered array:
12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58
Frequency
Cumulative
Frequency
BUSS1020
BUSS1020 57
WHY USE A FREQUENCY DISTRIBUTION?Condenses raw data into a more useful form
Allows a quick visual interpretation of the data
Enables determination of the major characteristics of the data, including where the data are concentrated / clustered
BUSS1020 58
FREQUENCY DISTRIBUTIONS: SOME TIPSDifferent class boundaries may provide different pictures for the same data (especially for smaller data sets)
Shifts in data concentration may show up when different class boundaries are chosen
As the size of the data set increases, the impact of alterations in the selection of class boundaries is greatly reduced
When comparing two or more data sets with different sample sizes, you should use either a relative frequency or a percentage distribution
BUSS1020 59
AGENDA
Introduction
Categorical Data Organising One Variable Categorical Data Visualising One Variable Categorical Data Organising Two Variable Categorical Data Visualising Two Variable Categorical Data
Numerical Data Organising Numerical DataVisualising One Variable Numerical Data Visualising Two Variable Numerical Data
Principles of Graphical Excellence
DCOVADCOVADCOVADCOVA
DCOVADCOVADCOVA
DCOVA
60
VISUALIZING NUMERICAL DATA BY USING GRAPHICAL DISPLAYS
Numerical Data
Ordered Array
Stem-and-LeafDisplay Histogram Polygon Ogive
Frequency Distributions and
Cumulative Distributions
DCOVA
Also
(not in this unit)
Bar chart (discrete data)
Boxplot
BUSS1020
Chap 2-60
BUSS1020 61
HANS ROSLING
Data Visualisation http://www.youtube.com/watch?v=jbkSRLYSojo
BUSS1020 62
ORGANIZING NUMERICAL DATA: HISTOGRAMA histogram organizes data into groups (bins); bin size reflects the percentage of data points in each group.
Example:
The Bureau of Meteorology (BOM) measures the rainfall (in mm.) in July 2011 for 20 Sydney suburbs:
24, 35, 17, 21, 24, 37, 26, 46, 58, 30, 32, 13, 12, 38, 41, 43, 44, 27, 53, 27
Chap 2-62
63
0
2
4
6
8
5 15 25 35 45 55 MoreF
req
ue
nc
y
Histogram: July 2011 rainfall
VISUALIZING NUMERICAL DATA: THE HISTOGRAM
Class Frequency
10 - 20 3 .15 15 20 - 30 6 .30 30 30 - 40 5 .25 25 40 - 50 4 .20 20 50 - 60 2 .10 10 Total 20 1.00 100
RelativeFrequency
Percentage
(In a percentage histogram the vertical axis would be defined to show the percentage of observations per class)
BUSS1020
Chap 2-63
BUSS1020 64
VISUALIZING NUMERICAL DATA: THE HISTOGRAMA vertical bar chart of the data in a frequency distribution is called a histogram.
In a histogram there are no gaps between adjacent bars for continuous data. There may be gaps for discrete data.
The class boundaries (or class midpoints) are shown on the horizontal axis.
The vertical axis is either frequency, relative frequency, or percentage.
The height of the bars represent the frequency, relative frequency, or percentage when considering identical width bins (intervals, class width).
Chap 2-64
BUSS1020 65
VISUALIZING NUMERICAL DATA: THE POLYGONA percentage polygon is formed by having the midpoint of each class represent the data in that class and then connecting the sequence of midpoints at their respective class percentages.
The cumulative percentage polygon, or ogive, displays the variable of interest along the X axis, and the cumulative percentages along the Y axis.
Useful when there are two or more groups to compare.
Chap 2-65
BUSS1020 66
Chap 2 pg 63
0
2
4
6
8
5 15 25 35 45 55 65
Freq
uenc
y
Frequency Polygon: July 2011 rainfall
VISUALIZING NUMERICAL DATA: THE FREQUENCY POLYGON
Class Midpoints of Rainfall
Class
10 - 20 15 3 20 - 30 25 6 30 - 40 35 5 40 - 50 45 4 50 - 60 55 2
FrequencyClass
Midpoint
(In a percentage polygon the vertical axis would be defined to show the percentage of observations per class)
BUSS1020 67
VISUALIZING NUMERICAL DATA: THE OGIVE (CUMULATIVE % POLYGON)
0
50
100
10 20 30 40 50 60
Cum
ulat
ive
Perc
enta
ge
Ogive: July 2011 rainfall
Class
10 - 20 10 0
20 - 30 20 15
30 - 40 30 45
40 - 50 40 70
50 - 60 50 90
60 - 70 60 100
% lessthan lowerboundary
Lower class
boundary
Lower Class Boundary
The percentage of observations less than each lower class boundary are plot, versus the lower class boundaries.
Chap 2 pg 64
BUSS1020 68
AGENDA
Introduction
Categorical Data Organising One Variable Categorical Data Visualising One Variable Categorical Data Organising Two Variable Categorical Data Visualising Two Variable Categorical Data
Numerical Data Organising Numerical Data Visualising One Variable Numerical DataVisualising Two Variable Numerical Data
Principles of Graphical Excellence
DCOVADCOVADCOVADCOVA
DCOVADCOVADCOVA
DCOVA
BUSS1020 69
Chap 2 pg 67VISUALIZING TWO NUMERICAL VARIABLES: THE SCATTER PLOTData consisting of paired observations taken from two numerical variables
One variable measured on the vertical axis; other variable measured on the horizontal axis
Scatter plots are used to examine possible relationships between two numerical variables
70
SCATTER PLOT EXAMPLE
Volume per day
Cost per day
23 125
26 140
29 146
33 160
38 167
42 170
50 188
55 195
60 200
BUSS1020
Cost per Day vs. Production Volume
0
50
100
150
200
250
20 30 40 50 60 70
Volume per Day
Co
st p
er D
ay
71
CEO compensation vs company stock return
CEO-compensation.txt Ex 2.89, page 72, Berenson.
BUSS1020
72
CEO compensation vs company stock return
CEO-compensation.txt Ex 2.89, page 72, Berenson.
BUSS1020
73
CEO compensation vs company stock return
CEO-compensation.txt Ex 2.89, page 72, Berenson.
BUSS1020
74
Sales vs Newspaper advertising ($000)
BUSS1020
BUSS1020 75
Chap 2 pg 67VISUALIZING TWO NUMERICAL VARIABLES: THE TIME-SERIES PLOTTime-series plots are used to study patterns in the values of a numeric variable over time.
The numeric variable is measured on the vertical axis and the time period is measured on the horizontal axis.
Frequency of observations is often on an issue.
BUSS1020 76
TIME SERIES PLOT EXAMPLE
Number of Franchises, 1996-2004
0
20
40
60
80
100
120
1994 1996 1998 2000 2002 2004 2006
Year
Numb
er o
f Fr
anch
ises
Year
Number of
Franchises
1996 43
1997 54
1998 60
1999 73
2000 82
2001 95
2002 107
2003 99
2004 95Here frequency is annual
BUSS1020 77
DAILY AORD INDEX: 1995-2013
0 500 1000 1500 2000 2500 3000 3500 4000 4500 50001000
2000
3000
4000
5000
6000
7000
Number of days
78
http://flowingdata.com/2015/07/02/changing-price-of-food-items-and-horizon-graphs/
BUSS1020
79
http://www.nytimes.com/interactive/2010/05/27/technology/20100527-apple.html?_r=0
BUSS1020
BUSS1020 80
AGENDA
Introduction
Categorical Data Organising One Variable Categorical Data Visualising One Variable Categorical Data Organising Two Variable Categorical Data Visualising Two Variable Categorical Data
Numerical Data Organising Numerical Data Visualising One Variable Numerical Data Visualising Two Variable Numerical Data
Principles of Graphical Excellence
DCOVADCOVADCOVADCOVA
DCOVADCOVADCOVA
DCOVA
BUSS1020 81
Chap 2 pg 76PRINCIPLES OF GRAPHICAL EXCELLENCEEvery graph should:not distort the data (story). not contain too much “chart junk” or “noise”. have all axes properly and clearly labelled. contain an informative title. be the simplest possible that tells the story. contain the source of the data. objectively and clearly convey the message or “story” in the data.
Further: 3D graphs should have a meaningful 3rd dimension. Usually 2D is sufficient.
The scale on the vertical axis should (usually) begin at zero.
82
http://www.perceptualedge.com/example14.php
Which plot is better?
‘83 ’93 ’03 ‘04 ‘05BUSS1020
BUSS1020 83
GRAPHICAL ERRORS: CHART JUNK, DISTORTION
1960: $1.00
1970: $1.60
1980: $3.10
1990: $3.80
Minimum Wage
Bad Presentation
Minimum Wage in the US
0
2
4
1960 1970 1980 1990
$
Good Presentation
84
http://www.perceptualedge.com/example18.php
BUSS1020
BUSS1020 85
GRAPHICAL ERRORS: NO RELATIVE BASIS
HD’s received by students.
HD’s received by students.
Bad Presentation
0
200
300
Y1 Y2 Y3 HO
Freq.
10%
30%
Y1 Y2 Y3 HO
Y1=First Year, Y2=Second Year, Y3=Third Year, HO=Honours
Source: University of Sydney Business School
100
20%
0%
%
Good Presentation
86
GRAPHICAL ERRORS: COMPRESSING THE VERTICAL AXIS
Good Presentation
Quarterly Sales Quarterly Sales
Bad Presentation
0
25
50
Q1 Q2 Q3 Q4
$
0
100
200
Q1 Q2 Q3 Q4
$
BUSS1020
BUSS1020 87
SUMMARYIntroduction
Categorical Data Organising One Variable Categorical Data: Summary Tables
Visualising One Variable Categorical Data: Bar Charts, Pie Charts, Pareto Charts
Organising Two Variable Categorical Data Contingency Tables
Visualising Two Variable Categorical Data Side-By-Side Charts, Time Series, Spatial Charts, 3-D Charts
Numerical Data Organising Numerical Data
Ordered Arrays, Frequency Distributions, Cumulative Distributions
Visualising One Variable Numerical Data Histograms, Polygons, Bar Charts, Ogives
Visualising Two Variable Numerical Data Scatter Plots, Time Series
Principles of Graphical Excellence