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excel代写-DPBS 1110
时间：2021-07-28
DPBS 1110 Evidence-Based Problem Solving
Unit 2: Problem Articulation and
Disaggregation
General housekeeping:
• Please switch your microphone to mute to avoid disruption to the class
• Use the chat channel to ask questions or make a comment, or raise your 'virtual' hand
• If you have poor internet, turn off your video
• Wait for your lecturer to start
1 2 3 4 5 6 7 8 9 10
LE
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TU
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10Unit 2:
Problem
articulation
and
disaggregation
Unit 3:
Frameworks
for ethical
decision-
making
Unit 4:
Understanding
problems –
Fact Gathering
Unit 5
Patterns,
biases,
hypothesising
effects
Unit 6:
Analysing the
issues –
Identifying
the causes
Unit 7:
Analysing the
issues – with
limited
evidence
Unit 8:
Problem
solving &
making
decisions
Unit 9
Evaluation
& communi
cation
Unit 1
Toolkit
application
Unit 2
Toolkit
application
Unit 3
Toolkit
application
Unit 4
Toolkit
application
Unit 5
Toolkit
application
Unit 6
Toolkit
application
Unit 7
Toolkit
application
Unit 8
Toolkit
application
11
Unit 1:
Problems &
evidence-
based
problem
solving
11
AS
SE
SS
M
EN
TS
DPBS1110 EVIDENCE-BASED PROBLEM SOLVING
Assessments
Unit 9
Toolkit
application
Assessment 2A:
Case Briefing Pack:
30%
Assessment 1A:
Associate level:
10%
Assessment 1B:
Expert level:
10%
Assessment 2B:
Case Business
Report: 50%
Please see DPBS1110 Assessment Guide for
details including exact deadlines
Excel Training Program:
10% + 10%
Case:
30% + 50%
Flexibility
Week
Revision
Unit
1-5
12
Revision
Unit
6 – 9
Feedback
Assessment
2A
Unit 2
Bullet Proof
Problem Solving
Framework
Problem
Solving
Tools
Case studies
and examples
• Logic trees (branch
framing, MECE,
prioritization)
• Descriptive statistics
• Graphs: Frequency
distributions, bar charts,
pie charts & histograms
• Furniture case
study
• UNSW Travel
• Birth data
Problem Articulation and Disaggregation
Defining the problem
• Starting point for great problem solving
• We will build on defining the problem by tapping into
problem articulation, especially how to use evidence to
understand what happened (e.g. summarising and
transforming data into useful information), which helps us
define the problem
Problem disaggregation
• Taking the problem apart helps us see potential ways to
solve it
• Any problem of real consequence is too complicated to
solve without breaking it down into logical parts that help
us understand the drivers or causes of the situation
Case study - Furniture Store
Will we be able to increase furniture sales?
Problem Disaggregation:
Information Toolbox - Logic Trees
• Provides clear visual representation of the problem so
we can understand component parts
• Done correctly, they are holistic (all relevant evidence
captured in the tree) so right questions can be asked
of the problem
• Leads to clear hypotheses (explanations/ideas) that
can be tested with evidence
Factor Logic Trees
Furniture store
First breakdown
Instore
Will we be able
to increase sales?
Online
Furniture store
Second breakdown
No. of store locations
Instore No. of product lines
instore
Level of customer
satisfaction
Will we be able
to increase sales?
Website useability
Online No. of delivery options
No. of product lines on
website
Factor Logic Trees
Furniture store
Third breakdown
No. of store locations
Instore No. of product lines instore
Level of customer satisfaction
Will we be able
to increase sales?
Website useability
Online No. of delivery options
No. of product lines on website
Ease of finding product line
Responsiveness of CSR
Sales representatives’
knowledge of product line
Logic Trees – Extend Branch Framing
Frame Key elements
Customer / shareholder /
employee
Competing perspectives
Price / volume Are there different products on the market? What about
the market share? What products are being adapted
by customers?
Regulation / incentives Will legal regulation, taxation, subsidies or nudging
policies change the outcome?
Equity / liberty Equality among citizens vs allowing more individual
freedom?
Near / long term Trade-offs in immediate future vs decades into the
future?
Financial/ non-financial Financial vs non-financial benefits?
Logic Trees – Prioritization to the Core Problem
Low High
Low
High
Ability to influence
Potential scale of impact
Statistical Toolbox – What evidence do
we have to articulate the problem?
• Sample vs Population
• Types of Statistical Data
• Cross-Section vs Time Series
Population vs Sample
• Population — the set of objects of interest
e.g. all UNSW students, all cars in a carpark
• Sample — a subset of the population
e.g. DPBS1110 students, cars in basement 1
Types of Statistical Data
Cross-sectional data & time-series data
• Cross-sectional data — data that are collected at a single
point in time
• Time-series data — data are collected over time (daily, weekly
monthly or quarterly), allowing us to see patterns over time
Using evidence to understand the problem
Perhaps sales might increase if
customers are satisfied with our
sales assistants?
Also, it may be useful to know
satisfaction
• By customer profile (age, gender,
individual/business customer,
etc.)
• and/or has satisfaction changed
over time?
Using evidence to understand the problem
Furniture Store Case Study – Customer Satisfaction
• 60,000 customers last year Population (what we are interested in)
• 3,000 customers completed customer satisfaction surveys for sales assistants
Sample (subset of the population that we have data for)
• Problem: How can we use sample information to gain some insights about the
population?
• Solution: We can describe the data (this unit) but also may want to use the
data to say something about the population (Use Inferential Statistics covered
in Unit 7 & 8)
Using evidence to understand the problem
Furniture Store Case Study – Customer Satisfaction
• Have 3,000 rows of customer satisfaction data in an Excel spreadsheet
• How do we use these data to solve our problem?
• First, we need to recognize different types of data as that has implications for
how the data are summarized
Survey Completion Data
and Time
Sales
Assistant
Customer Satisfaction -
Service
Customer
Gender
Number of Items
Purchased Sales Value
12/12/2020 K Jones 5. Excellent Male 2 $ 99.95
12/12/2020 K Jones 5. Excellent Male 1 $ 1,500.00
12/12/2020 H Smith 5. Excellent Female 3 $ 12,000.00
12/12/2020 H Smith 3. Good Female 1 $ 500.00
16/12/2020 H Smith 3. Good Male 5 $ 1,335.00
16/12/2020 B Clark 4. Great Male 6 $ 2,449.95
16/12/2020 B Clark 2. Fair Female 1 $ 129.95
19/12/2020 B Clark 2. Fair Female 3 $ 359.00
20/12/2020 B Clark 1. Poor Male 4 $ 450.00
… … … … … …
Types of Statistical Data
Types of Data
Survey Completion Data
and Time
Sales
Assistant
Customer Satisfaction -
Service
Customer
Gender
Number of Items
Purchased Sales Value
12/12/2020 K Jones 5. Excellent Male 2 $ 99.95
12/12/2020 K Jones 5. Excellent Male 1 $ 1,500.00
12/12/2020 H Smith 5. Excellent Female 3 $ 12,000.00
12/12/2020 H Smith 3. Good Female 1 $ 500.00
16/12/2020 H Smith 3. Good Male 5 $ 1,335.00
16/12/2020 B Clark 4. Great Male 6 $ 2,449.95
16/12/2020 B Clark 2. Fair Female 1 $ 129.95
19/12/2020 B Clark 2. Fair Female 3 $ 359.00
20/12/2020 B Clark 1. Poor Male 4 $ 450.00
… … … … … …
A variable (each column
represents a variable
here) is a characteristic
of a population or of a
sample from a population
Observations
In order to apply statistical analyses directly to qualitative data, we
must convert it somehow to quantitative data (e.g. convert customer
satisfaction Excellent 5 Great 4, Good 3, Fair 2, Poor 1)
Variable type
A data set contains observations on variables (e.g. the table above shows the customer satisfaction data set ).
Types of Data
0
50
100
150
200
250
300
350
Total number of customers served
by K Jones
Time series data consist of measurements of
the same concept at different points in time
Cross sectional data consist of
measurements of one or more concepts
at a single point in time
• In July, how many customers did
each assistant serve?
The type of data influences what sort of
analysis and presentation works best
• The time series plot is a convenient
summary but note you have a choice
of what level of aggregation to use
• Using monthly data for customers
served makes sense as it highlights
the end-of-year peak in sales
Using evidence to understand the problem
Furniture Store Case Study – Customer Satisfaction
Are customers satisfied with our sales assistants?
(e.g. H Smith received Excellent, Good ratings from customers; B Clark received
Great, Fair, and Poor ratings from customers, but there are 3,000 observations!)
Solution: Summarise the data possibly with visualizations!
Survey Completion Data
and Time
Sales
Assistant
Customer Satisfaction -
Service
Customer
Gender
Number of Items
Purchased Sales Value
12/12/2020 K Jones 5. Excellent Male 2 $ 99.95
12/12/2020 K Jones 5. Excellent Male 1 $ 1,500.00
12/12/2020 H Smith 5. Excellent Female 3 $ 12,000.00
12/12/2020 H Smith 3. Good Female 1 $ 500.00
16/12/2020 H Smith 3. Good Male 5 $ 1,335.00
16/12/2020 B Clark 4. Great Male 6 $ 2,449.95
16/12/2020 B Clark 2. Fair Female 1 $ 129.95
19/12/2020 B Clark 2. Fair Female 3 $ 359.00
20/12/2020 B Clark 1. Poor Male 4 $ 450.00
… … … … … …
Using evidence to understand the problem
Furniture Store Case Study – Customer Satisfaction
Visualising Data
0.00
1.00
2.00
3.00
4.00
5.00
Average Customer Satisfaction
K Jones H Smith B Clark
K Jones appears to have the highest average customer satisfaction ratings over
time. Visualising data helps us to generate this insight. Now that we’ve summarized
the data, do we better understand the problem?
Using evidence to understand the problem
Furniture Store Case Study – Customer Satisfaction
Further notes
• You need to be able to produce graphs as in previous slide
o See the associated tutorials
• Summarising data helps to highlight key features of the data but
there are many choices in how this is done
o Some of these are covered next
o DPBS1190 builds upon this foundation
• Evidence other than the survey data would also be relevant
• Online reviews (e.g. Google review)
• Interviews and performance reports from managers
Statistical Toolbox – Disaggregate the
problem with descriptive statistics
• One variable
• Frequency distributions, bar charts, pie charts & histograms
• Shapes of distributions
• Measures of central tendency or location
• Measures of dispersion or spread
• Two variables (mostly done in Unit 4):
• Scatter plots and cross-tabulations to describe bivariate relations
• Measures of association i.e. correlation and covariance
• Introduction to linear regression
Using evidence to understand the problem
UNSW Travel Case Study
• UNSW routinely surveys staff & students to monitor travel
patterns & trends
• Such data provides evidence to inform operational problem solving &
forward planning
• See 2019 survey results here
• Similar analysis will be provided using the 2011 data
• Frequency distributions, bar charts & pie charts will be used
Frequency Distributions, Bar Charts and
Pie Charts
• Bar chart provides graphical
representation of frequency
distribution of mode of transport
• 2011 survey a sample of 5,881
responses
• 47 (0.8%) Resident
• 628 (10.7%) Walk
• 210 (3.6%) Cycle
• 1,032 (17.5%) Car
• 1,188 (20.2%) Bus
• 2,669 (45.4%) Bus and Train
• 107 (1.8%) Other
0
500
1000
1500
2000
2500
3000
Resident Walk Cycle Car Bus Bus &
Train
Other
Bar chart of mode of transport to UNSW Campus
Frequency Distributions, Bar Charts and
Pie Charts
• Pie charts show relative
frequencies more explicitly Resident, 0.8%
Walk,
10.7%
Cycle, 3.6%
Car, 17.5%
Bus, 20.2%
Bus & Train,
45.4%
Other, 1.8%
Pie chart of mode of transport to UNSW Campus
Resident
Walk
Cycle
Car
Bus
Bus & Train
Other
Frequency Distributions, Bar Charts and
Pie Charts
0
500
1000
1500
2000
2500
3000
Fr
eq
ue
nc
y
Mode of transport by commuter type
Staff Students
Commuter Type
Mode Staff Student Total
Resident 0 47 47
Walk 97 531 628
Cycle 52 158 210
Car 472 560 1032
Bus 186 1002 1188
Bus & Train 230 2439 2669
Other 25 82 107
Total 1062 4819 5881
Frequency Distributions, Bar Charts and
Pie Charts
• What does the previous
bar chart highlight?
• Is there a better
representation?
0.00
0.10
0.20
0.30
0.40
0.50
0.60
Resident Walk Bike Car Bus Bus &
train
Other
Re
la
tiv
e
fre
qu
en
cy
b
y
ty
pe
Mode of transport by commuter type - Example 2
Staff Students
Using evidence to understand the problem
UNSW Travel Case Study
• Such surveys provided evidence base supporting the need for
light rail to service travel to UNSW
• Will eventually provide evidence about the impact of light rail (a
before & after comparison)
• Need to recognize that there are choices in how the same
data can be summarized
• These choices need to be guided by the problem being solved
• Also need to recognize that data will always have limitations
• Covered in Unit 7 and 8
Statistical Toolbox
Furniture store example: ‘Are customers satisfied
with our sales assistants?’
• Different evidence - sales performance - to look at the same
question
Disaggregate the problem with descriptive
statistics
• Histograms to determine symmetry, skewness, modal classes
& outliers
• Comparing measures of central tendency and spread
Using evidence for a refined problem
Furniture Store Case Study – Sales Performance
Are sales assistant different in terms of their sales performance?
• Started with general problem of monitoring staff performance
• Initially looked at customer satisfaction but equally important to monitor sales
as a performance measure
• Data are available on the sales amount to individual customers and the number
of items sold so choices in what to use
o Could also use these two variables to construct the average purchase amount per
customer
• Will develop an evidence base comparing the different sales assistants in
terms of these variables
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Are sales different depending on where customers heard about the store?
• Started with general problem of monitoring staff performance
• Initially looked at customer satisfaction but equally important to monitor sales as
a performance measure & how that relates to marketing
• Data are available from a different survey of customers who purchased furniture
o Focus on actual sales (spend) & amount willing to spend (budget) when entering
store
o Could also use these two variables to construct the amount spent as a share of the
budget
o Also know where they said they heard about the store
• Develop an evidence base comparing sales in terms of where the customer
heard about the store concentrating on web presence
Histograms
• Suppose data are ordinal (whether discrete or continuous)
• Obvious categories for the data values may not exist
• Can create categories or classes by defining lower & upper class
limits
• Categories need to be mutually exclusive and exhaustive
• How many categories? (Excel calls them bins)
• Too many doesn’t summarize
• Too few no information
• No set rules on number of bins, although having more observations
means one generally wants more bins
• Bins need not be of equal width & may be open-ended at the top or
bottom
Histograms
0
5
10
15
20
25
30
35
40
45
50
12
50
25
00
37
50
50
00
62
50
75
00
87
50
10
00
0
11
25
0
12
50
0
13
75
0
15
00
0
M
or
e
Fr
eq
ue
nc
y
spend
Histogram for amount spent by customer
0
50
100
150
200
250
15
00
0
30
00
0
45
00
0
60
00
0
75
00
0
90
00
0
10
50
00
12
00
00
13
50
00
15
00
00
16
50
00
18
00
00
19
50
00
M
or
e
Fr
eq
ue
nc
y
budget
Histogram for budget of customer
Histograms
• Budget histogram is not
informative for bulk of
data because of several
customers with relatively
large budgets (outliers)
• Consider trimmed sample
excluding 4 largest
observations
0
10
20
30
40
50
60
70
Fr
eq
ue
nc
y
budget
Histogram for budget with trimmed sample
Describing Histograms
• Symmetry (or lack thereof)
• Left half of a symmetric histogram is a mirror image of right half
• Famous ‘bell-shaped curve’ (normal distribution) is symmetric
• Skewness
• A feature of an asymmetric histogram
• Long tail to the right: positively skewed
• Long tail to the left: negatively skewed
• May be associated with outliers
• Number of modal classes/bins
• The modal class is the class with highest frequency
• Histograms may be unimodal or multimodal
39
Describing Histograms
• Distribution of budget
skewed by outliers
• Distribution of
spend/budget has no
obvious outliers but is
positively skewed
• Notice some customers
spend more than their
initial budget
40
0
10
20
30
40
50
60
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 More
Fr
eq
ue
nc
y
spendratio
Histogram for spending as a ratio of budget
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Further Analysis
• Comparing distributions of spend & budget is informative but
further summarization is helpful
• Providing numerical summaries is useful
• How do spend & budget compare “on average”
• Different summary statistics can answer this type of question
• Most common measures of “location” being the mean & median
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Further Analysis
• The means for our sample indicate
• spend: $7529; budget: $22,119; & spend/budget: 0.395
• On average customers who make purchases spend about 40% of their
budget in the store
• From histograms, budget is very skewed due to large outliers, so
does it matter much if we report the medians?
• spend: $7000; budget: $21,000; & spend/budget: 0.366
• In each case, medianmessage unchanged – customers spend a lot less than their
budget
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Further Analysis
• Another characteristic of the distributions is the spread –
how much variation is there in the average sale?
• Most common measure of dispersion or spread is the variance (or
the standard deviation)
• Standard deviations
• spend: $3,939; budget: $15,885; & spend/budget: 0.250
• budget is relatively more dispersed, again because of outliers
• Standard deviation for trimmed sample is only $5,252
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Are sales different depending on where customers heard
about the store?
• As most interested in marketing via the web define = 1 if
customer was aware of store from web search or store website
& zero otherwise
o If = 1 then means are spend: $7,481; budget: $23,716
o If = 0 then means are spend: $7,547; budget: $21,509
• Customers attracted via the web tend to have larger budgets
but then tend to spend less on average
oDoes this present a problem?
Using evidence for an extended problem
Furniture Store Case study – Sales Performance
Are sales different depending on where customers heard
about the store?
• Stressed survey is a sample from the population of sales data
• Can we confidently say that customers attracted via the web tend to
have larger budgets but then tend to spend less on average
• Such a conclusion relates to a comparison of population means
whereas what was provided was a comparison of sample means
• Are differences observed in the sample data “real” or simply a matter
of random variation not related to how the customer became aware
of the store?
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Are sales different depending on where customers heard
about the store?
• Making comparisons of population means is covered in
statistical inference that will be introduced later in the course
• In the language of inference, hypotheses will be developed & tested
• For the moment, our evidence base is descriptive which is useful but
only part of the answer
Re-cap
Defining the problem
• Have stressed problem articulation, especially how to use
quantitative evidence to understand what happened
• Think in terms of setting the scene by using data to obtain
stylized facts
• Basic descriptive statistics is important here
Problem disaggregation
• Stressed importance of breaking down problems into constituent
parts
• These parts become amenable to analysis with some
statistical tools that were illustrated
• Yes, you may need to synthesize all the parts but that comes
later in the course
Thank you
The lecture recording will be made
available in your Moodle course site.
message unchanged – customers spend a lot less than their
budget
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Further Analysis
• Another characteristic of the distributions is the spread –
how much variation is there in the average sale?
• Most common measure of dispersion or spread is the variance (or
the standard deviation)
• Standard deviations
• spend: $3,939; budget: $15,885; & spend/budget: 0.250
• budget is relatively more dispersed, again because of outliers
• Standard deviation for trimmed sample is only $5,252
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Are sales different depending on where customers heard
about the store?
• As most interested in marketing via the web define = 1 if
customer was aware of store from web search or store website
& zero otherwise
o If = 1 then means are spend: $7,481; budget: $23,716
o If = 0 then means are spend: $7,547; budget: $21,509
• Customers attracted via the web tend to have larger budgets
but then tend to spend less on average
oDoes this present a problem?
Using evidence for an extended problem
Furniture Store Case study – Sales Performance
Are sales different depending on where customers heard
about the store?
• Stressed survey is a sample from the population of sales data
• Can we confidently say that customers attracted via the web tend to
have larger budgets but then tend to spend less on average
• Such a conclusion relates to a comparison of population means
whereas what was provided was a comparison of sample means
• Are differences observed in the sample data “real” or simply a matter
of random variation not related to how the customer became aware
of the store?
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Are sales different depending on where customers heard
about the store?
• Making comparisons of population means is covered in
statistical inference that will be introduced later in the course
• In the language of inference, hypotheses will be developed & tested
• For the moment, our evidence base is descriptive which is useful but
only part of the answer
Re-cap
Defining the problem
• Have stressed problem articulation, especially how to use
quantitative evidence to understand what happened
• Think in terms of setting the scene by using data to obtain
stylized facts
• Basic descriptive statistics is important here
Problem disaggregation
• Stressed importance of breaking down problems into constituent
parts
• These parts become amenable to analysis with some
statistical tools that were illustrated
• Yes, you may need to synthesize all the parts but that comes
later in the course
Thank you
The lecture recording will be made
available in your Moodle course site.
学霸联盟
Unit 2: Problem Articulation and
Disaggregation
General housekeeping:
• Please switch your microphone to mute to avoid disruption to the class
• Use the chat channel to ask questions or make a comment, or raise your 'virtual' hand
• If you have poor internet, turn off your video
• Wait for your lecturer to start
1 2 3 4 5 6 7 8 9 10
LE
CT
UR
ES
TU
TO
RI
AL
S
10Unit 2:
Problem
articulation
and
disaggregation
Unit 3:
Frameworks
for ethical
decision-
making
Unit 4:
Understanding
problems –
Fact Gathering
Unit 5
Patterns,
biases,
hypothesising
effects
Unit 6:
Analysing the
issues –
Identifying
the causes
Unit 7:
Analysing the
issues – with
limited
evidence
Unit 8:
Problem
solving &
making
decisions
Unit 9
Evaluation
& communi
cation
Unit 1
Toolkit
application
Unit 2
Toolkit
application
Unit 3
Toolkit
application
Unit 4
Toolkit
application
Unit 5
Toolkit
application
Unit 6
Toolkit
application
Unit 7
Toolkit
application
Unit 8
Toolkit
application
11
Unit 1:
Problems &
evidence-
based
problem
solving
11
AS
SE
SS
M
EN
TS
DPBS1110 EVIDENCE-BASED PROBLEM SOLVING
Assessments
Unit 9
Toolkit
application
Assessment 2A:
Case Briefing Pack:
30%
Assessment 1A:
Associate level:
10%
Assessment 1B:
Expert level:
10%
Assessment 2B:
Case Business
Report: 50%
Please see DPBS1110 Assessment Guide for
details including exact deadlines
Excel Training Program:
10% + 10%
Case:
30% + 50%
Flexibility
Week
Revision
Unit
1-5
12
Revision
Unit
6 – 9
Feedback
Assessment
2A
Unit 2
Bullet Proof
Problem Solving
Framework
Problem
Solving
Tools
Case studies
and examples
• Logic trees (branch
framing, MECE,
prioritization)
• Descriptive statistics
• Graphs: Frequency
distributions, bar charts,
pie charts & histograms
• Furniture case
study
• UNSW Travel
• Birth data
Problem Articulation and Disaggregation
Defining the problem
• Starting point for great problem solving
• We will build on defining the problem by tapping into
problem articulation, especially how to use evidence to
understand what happened (e.g. summarising and
transforming data into useful information), which helps us
define the problem
Problem disaggregation
• Taking the problem apart helps us see potential ways to
solve it
• Any problem of real consequence is too complicated to
solve without breaking it down into logical parts that help
us understand the drivers or causes of the situation
Case study - Furniture Store
Will we be able to increase furniture sales?
Problem Disaggregation:
Information Toolbox - Logic Trees
• Provides clear visual representation of the problem so
we can understand component parts
• Done correctly, they are holistic (all relevant evidence
captured in the tree) so right questions can be asked
of the problem
• Leads to clear hypotheses (explanations/ideas) that
can be tested with evidence
Factor Logic Trees
Furniture store
First breakdown
Instore
Will we be able
to increase sales?
Online
Furniture store
Second breakdown
No. of store locations
Instore No. of product lines
instore
Level of customer
satisfaction
Will we be able
to increase sales?
Website useability
Online No. of delivery options
No. of product lines on
website
Factor Logic Trees
Furniture store
Third breakdown
No. of store locations
Instore No. of product lines instore
Level of customer satisfaction
Will we be able
to increase sales?
Website useability
Online No. of delivery options
No. of product lines on website
Ease of finding product line
Responsiveness of CSR
Sales representatives’
knowledge of product line
Logic Trees – Extend Branch Framing
Frame Key elements
Customer / shareholder /
employee
Competing perspectives
Price / volume Are there different products on the market? What about
the market share? What products are being adapted
by customers?
Regulation / incentives Will legal regulation, taxation, subsidies or nudging
policies change the outcome?
Equity / liberty Equality among citizens vs allowing more individual
freedom?
Near / long term Trade-offs in immediate future vs decades into the
future?
Financial/ non-financial Financial vs non-financial benefits?
Logic Trees – Prioritization to the Core Problem
Low High
Low
High
Ability to influence
Potential scale of impact
Statistical Toolbox – What evidence do
we have to articulate the problem?
• Sample vs Population
• Types of Statistical Data
• Cross-Section vs Time Series
Population vs Sample
• Population — the set of objects of interest
e.g. all UNSW students, all cars in a carpark
• Sample — a subset of the population
e.g. DPBS1110 students, cars in basement 1
Types of Statistical Data
Cross-sectional data & time-series data
• Cross-sectional data — data that are collected at a single
point in time
• Time-series data — data are collected over time (daily, weekly
monthly or quarterly), allowing us to see patterns over time
Using evidence to understand the problem
Perhaps sales might increase if
customers are satisfied with our
sales assistants?
Also, it may be useful to know
satisfaction
• By customer profile (age, gender,
individual/business customer,
etc.)
• and/or has satisfaction changed
over time?
Using evidence to understand the problem
Furniture Store Case Study – Customer Satisfaction
• 60,000 customers last year Population (what we are interested in)
• 3,000 customers completed customer satisfaction surveys for sales assistants
Sample (subset of the population that we have data for)
• Problem: How can we use sample information to gain some insights about the
population?
• Solution: We can describe the data (this unit) but also may want to use the
data to say something about the population (Use Inferential Statistics covered
in Unit 7 & 8)
Using evidence to understand the problem
Furniture Store Case Study – Customer Satisfaction
• Have 3,000 rows of customer satisfaction data in an Excel spreadsheet
• How do we use these data to solve our problem?
• First, we need to recognize different types of data as that has implications for
how the data are summarized
Survey Completion Data
and Time
Sales
Assistant
Customer Satisfaction -
Service
Customer
Gender
Number of Items
Purchased Sales Value
12/12/2020 K Jones 5. Excellent Male 2 $ 99.95
12/12/2020 K Jones 5. Excellent Male 1 $ 1,500.00
12/12/2020 H Smith 5. Excellent Female 3 $ 12,000.00
12/12/2020 H Smith 3. Good Female 1 $ 500.00
16/12/2020 H Smith 3. Good Male 5 $ 1,335.00
16/12/2020 B Clark 4. Great Male 6 $ 2,449.95
16/12/2020 B Clark 2. Fair Female 1 $ 129.95
19/12/2020 B Clark 2. Fair Female 3 $ 359.00
20/12/2020 B Clark 1. Poor Male 4 $ 450.00
… … … … … …
Types of Statistical Data
Types of Data
Survey Completion Data
and Time
Sales
Assistant
Customer Satisfaction -
Service
Customer
Gender
Number of Items
Purchased Sales Value
12/12/2020 K Jones 5. Excellent Male 2 $ 99.95
12/12/2020 K Jones 5. Excellent Male 1 $ 1,500.00
12/12/2020 H Smith 5. Excellent Female 3 $ 12,000.00
12/12/2020 H Smith 3. Good Female 1 $ 500.00
16/12/2020 H Smith 3. Good Male 5 $ 1,335.00
16/12/2020 B Clark 4. Great Male 6 $ 2,449.95
16/12/2020 B Clark 2. Fair Female 1 $ 129.95
19/12/2020 B Clark 2. Fair Female 3 $ 359.00
20/12/2020 B Clark 1. Poor Male 4 $ 450.00
… … … … … …
A variable (each column
represents a variable
here) is a characteristic
of a population or of a
sample from a population
Observations
In order to apply statistical analyses directly to qualitative data, we
must convert it somehow to quantitative data (e.g. convert customer
satisfaction Excellent 5 Great 4, Good 3, Fair 2, Poor 1)
Variable type
A data set contains observations on variables (e.g. the table above shows the customer satisfaction data set ).
Types of Data
0
50
100
150
200
250
300
350
Total number of customers served
by K Jones
Time series data consist of measurements of
the same concept at different points in time
Cross sectional data consist of
measurements of one or more concepts
at a single point in time
• In July, how many customers did
each assistant serve?
The type of data influences what sort of
analysis and presentation works best
• The time series plot is a convenient
summary but note you have a choice
of what level of aggregation to use
• Using monthly data for customers
served makes sense as it highlights
the end-of-year peak in sales
Using evidence to understand the problem
Furniture Store Case Study – Customer Satisfaction
Are customers satisfied with our sales assistants?
(e.g. H Smith received Excellent, Good ratings from customers; B Clark received
Great, Fair, and Poor ratings from customers, but there are 3,000 observations!)
Solution: Summarise the data possibly with visualizations!
Survey Completion Data
and Time
Sales
Assistant
Customer Satisfaction -
Service
Customer
Gender
Number of Items
Purchased Sales Value
12/12/2020 K Jones 5. Excellent Male 2 $ 99.95
12/12/2020 K Jones 5. Excellent Male 1 $ 1,500.00
12/12/2020 H Smith 5. Excellent Female 3 $ 12,000.00
12/12/2020 H Smith 3. Good Female 1 $ 500.00
16/12/2020 H Smith 3. Good Male 5 $ 1,335.00
16/12/2020 B Clark 4. Great Male 6 $ 2,449.95
16/12/2020 B Clark 2. Fair Female 1 $ 129.95
19/12/2020 B Clark 2. Fair Female 3 $ 359.00
20/12/2020 B Clark 1. Poor Male 4 $ 450.00
… … … … … …
Using evidence to understand the problem
Furniture Store Case Study – Customer Satisfaction
Visualising Data
0.00
1.00
2.00
3.00
4.00
5.00
Average Customer Satisfaction
K Jones H Smith B Clark
K Jones appears to have the highest average customer satisfaction ratings over
time. Visualising data helps us to generate this insight. Now that we’ve summarized
the data, do we better understand the problem?
Using evidence to understand the problem
Furniture Store Case Study – Customer Satisfaction
Further notes
• You need to be able to produce graphs as in previous slide
o See the associated tutorials
• Summarising data helps to highlight key features of the data but
there are many choices in how this is done
o Some of these are covered next
o DPBS1190 builds upon this foundation
• Evidence other than the survey data would also be relevant
• Online reviews (e.g. Google review)
• Interviews and performance reports from managers
Statistical Toolbox – Disaggregate the
problem with descriptive statistics
• One variable
• Frequency distributions, bar charts, pie charts & histograms
• Shapes of distributions
• Measures of central tendency or location
• Measures of dispersion or spread
• Two variables (mostly done in Unit 4):
• Scatter plots and cross-tabulations to describe bivariate relations
• Measures of association i.e. correlation and covariance
• Introduction to linear regression
Using evidence to understand the problem
UNSW Travel Case Study
• UNSW routinely surveys staff & students to monitor travel
patterns & trends
• Such data provides evidence to inform operational problem solving &
forward planning
• See 2019 survey results here
• Similar analysis will be provided using the 2011 data
• Frequency distributions, bar charts & pie charts will be used
Frequency Distributions, Bar Charts and
Pie Charts
• Bar chart provides graphical
representation of frequency
distribution of mode of transport
• 2011 survey a sample of 5,881
responses
• 47 (0.8%) Resident
• 628 (10.7%) Walk
• 210 (3.6%) Cycle
• 1,032 (17.5%) Car
• 1,188 (20.2%) Bus
• 2,669 (45.4%) Bus and Train
• 107 (1.8%) Other
0
500
1000
1500
2000
2500
3000
Resident Walk Cycle Car Bus Bus &
Train
Other
Bar chart of mode of transport to UNSW Campus
Frequency Distributions, Bar Charts and
Pie Charts
• Pie charts show relative
frequencies more explicitly Resident, 0.8%
Walk,
10.7%
Cycle, 3.6%
Car, 17.5%
Bus, 20.2%
Bus & Train,
45.4%
Other, 1.8%
Pie chart of mode of transport to UNSW Campus
Resident
Walk
Cycle
Car
Bus
Bus & Train
Other
Frequency Distributions, Bar Charts and
Pie Charts
0
500
1000
1500
2000
2500
3000
Fr
eq
ue
nc
y
Mode of transport by commuter type
Staff Students
Commuter Type
Mode Staff Student Total
Resident 0 47 47
Walk 97 531 628
Cycle 52 158 210
Car 472 560 1032
Bus 186 1002 1188
Bus & Train 230 2439 2669
Other 25 82 107
Total 1062 4819 5881
Frequency Distributions, Bar Charts and
Pie Charts
• What does the previous
bar chart highlight?
• Is there a better
representation?
0.00
0.10
0.20
0.30
0.40
0.50
0.60
Resident Walk Bike Car Bus Bus &
train
Other
Re
la
tiv
e
fre
qu
en
cy
b
y
ty
pe
Mode of transport by commuter type - Example 2
Staff Students
Using evidence to understand the problem
UNSW Travel Case Study
• Such surveys provided evidence base supporting the need for
light rail to service travel to UNSW
• Will eventually provide evidence about the impact of light rail (a
before & after comparison)
• Need to recognize that there are choices in how the same
data can be summarized
• These choices need to be guided by the problem being solved
• Also need to recognize that data will always have limitations
• Covered in Unit 7 and 8
Statistical Toolbox
Furniture store example: ‘Are customers satisfied
with our sales assistants?’
• Different evidence - sales performance - to look at the same
question
Disaggregate the problem with descriptive
statistics
• Histograms to determine symmetry, skewness, modal classes
& outliers
• Comparing measures of central tendency and spread
Using evidence for a refined problem
Furniture Store Case Study – Sales Performance
Are sales assistant different in terms of their sales performance?
• Started with general problem of monitoring staff performance
• Initially looked at customer satisfaction but equally important to monitor sales
as a performance measure
• Data are available on the sales amount to individual customers and the number
of items sold so choices in what to use
o Could also use these two variables to construct the average purchase amount per
customer
• Will develop an evidence base comparing the different sales assistants in
terms of these variables
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Are sales different depending on where customers heard about the store?
• Started with general problem of monitoring staff performance
• Initially looked at customer satisfaction but equally important to monitor sales as
a performance measure & how that relates to marketing
• Data are available from a different survey of customers who purchased furniture
o Focus on actual sales (spend) & amount willing to spend (budget) when entering
store
o Could also use these two variables to construct the amount spent as a share of the
budget
o Also know where they said they heard about the store
• Develop an evidence base comparing sales in terms of where the customer
heard about the store concentrating on web presence
Histograms
• Suppose data are ordinal (whether discrete or continuous)
• Obvious categories for the data values may not exist
• Can create categories or classes by defining lower & upper class
limits
• Categories need to be mutually exclusive and exhaustive
• How many categories? (Excel calls them bins)
• Too many doesn’t summarize
• Too few no information
• No set rules on number of bins, although having more observations
means one generally wants more bins
• Bins need not be of equal width & may be open-ended at the top or
bottom
Histograms
0
5
10
15
20
25
30
35
40
45
50
12
50
25
00
37
50
50
00
62
50
75
00
87
50
10
00
0
11
25
0
12
50
0
13
75
0
15
00
0
M
or
e
Fr
eq
ue
nc
y
spend
Histogram for amount spent by customer
0
50
100
150
200
250
15
00
0
30
00
0
45
00
0
60
00
0
75
00
0
90
00
0
10
50
00
12
00
00
13
50
00
15
00
00
16
50
00
18
00
00
19
50
00
M
or
e
Fr
eq
ue
nc
y
budget
Histogram for budget of customer
Histograms
• Budget histogram is not
informative for bulk of
data because of several
customers with relatively
large budgets (outliers)
• Consider trimmed sample
excluding 4 largest
observations
0
10
20
30
40
50
60
70
Fr
eq
ue
nc
y
budget
Histogram for budget with trimmed sample
Describing Histograms
• Symmetry (or lack thereof)
• Left half of a symmetric histogram is a mirror image of right half
• Famous ‘bell-shaped curve’ (normal distribution) is symmetric
• Skewness
• A feature of an asymmetric histogram
• Long tail to the right: positively skewed
• Long tail to the left: negatively skewed
• May be associated with outliers
• Number of modal classes/bins
• The modal class is the class with highest frequency
• Histograms may be unimodal or multimodal
39
Describing Histograms
• Distribution of budget
skewed by outliers
• Distribution of
spend/budget has no
obvious outliers but is
positively skewed
• Notice some customers
spend more than their
initial budget
40
0
10
20
30
40
50
60
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 More
Fr
eq
ue
nc
y
spendratio
Histogram for spending as a ratio of budget
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Further Analysis
• Comparing distributions of spend & budget is informative but
further summarization is helpful
• Providing numerical summaries is useful
• How do spend & budget compare “on average”
• Different summary statistics can answer this type of question
• Most common measures of “location” being the mean & median
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Further Analysis
• The means for our sample indicate
• spend: $7529; budget: $22,119; & spend/budget: 0.395
• On average customers who make purchases spend about 40% of their
budget in the store
• From histograms, budget is very skewed due to large outliers, so
does it matter much if we report the medians?
• spend: $7000; budget: $21,000; & spend/budget: 0.366
• In each case, median
budget
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Further Analysis
• Another characteristic of the distributions is the spread –
how much variation is there in the average sale?
• Most common measure of dispersion or spread is the variance (or
the standard deviation)
• Standard deviations
• spend: $3,939; budget: $15,885; & spend/budget: 0.250
• budget is relatively more dispersed, again because of outliers
• Standard deviation for trimmed sample is only $5,252
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Are sales different depending on where customers heard
about the store?
• As most interested in marketing via the web define = 1 if
customer was aware of store from web search or store website
& zero otherwise
o If = 1 then means are spend: $7,481; budget: $23,716
o If = 0 then means are spend: $7,547; budget: $21,509
• Customers attracted via the web tend to have larger budgets
but then tend to spend less on average
oDoes this present a problem?
Using evidence for an extended problem
Furniture Store Case study – Sales Performance
Are sales different depending on where customers heard
about the store?
• Stressed survey is a sample from the population of sales data
• Can we confidently say that customers attracted via the web tend to
have larger budgets but then tend to spend less on average
• Such a conclusion relates to a comparison of population means
whereas what was provided was a comparison of sample means
• Are differences observed in the sample data “real” or simply a matter
of random variation not related to how the customer became aware
of the store?
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Are sales different depending on where customers heard
about the store?
• Making comparisons of population means is covered in
statistical inference that will be introduced later in the course
• In the language of inference, hypotheses will be developed & tested
• For the moment, our evidence base is descriptive which is useful but
only part of the answer
Re-cap
Defining the problem
• Have stressed problem articulation, especially how to use
quantitative evidence to understand what happened
• Think in terms of setting the scene by using data to obtain
stylized facts
• Basic descriptive statistics is important here
Problem disaggregation
• Stressed importance of breaking down problems into constituent
parts
• These parts become amenable to analysis with some
statistical tools that were illustrated
• Yes, you may need to synthesize all the parts but that comes
later in the course
Thank you
The lecture recording will be made
available in your Moodle course site.
budget
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Further Analysis
• Another characteristic of the distributions is the spread –
how much variation is there in the average sale?
• Most common measure of dispersion or spread is the variance (or
the standard deviation)
• Standard deviations
• spend: $3,939; budget: $15,885; & spend/budget: 0.250
• budget is relatively more dispersed, again because of outliers
• Standard deviation for trimmed sample is only $5,252
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Are sales different depending on where customers heard
about the store?
• As most interested in marketing via the web define = 1 if
customer was aware of store from web search or store website
& zero otherwise
o If = 1 then means are spend: $7,481; budget: $23,716
o If = 0 then means are spend: $7,547; budget: $21,509
• Customers attracted via the web tend to have larger budgets
but then tend to spend less on average
oDoes this present a problem?
Using evidence for an extended problem
Furniture Store Case study – Sales Performance
Are sales different depending on where customers heard
about the store?
• Stressed survey is a sample from the population of sales data
• Can we confidently say that customers attracted via the web tend to
have larger budgets but then tend to spend less on average
• Such a conclusion relates to a comparison of population means
whereas what was provided was a comparison of sample means
• Are differences observed in the sample data “real” or simply a matter
of random variation not related to how the customer became aware
of the store?
Using evidence for an extended problem
Furniture Store Case Study – Sales Performance
Are sales different depending on where customers heard
about the store?
• Making comparisons of population means is covered in
statistical inference that will be introduced later in the course
• In the language of inference, hypotheses will be developed & tested
• For the moment, our evidence base is descriptive which is useful but
only part of the answer
Re-cap
Defining the problem
• Have stressed problem articulation, especially how to use
quantitative evidence to understand what happened
• Think in terms of setting the scene by using data to obtain
stylized facts
• Basic descriptive statistics is important here
Problem disaggregation
• Stressed importance of breaking down problems into constituent
parts
• These parts become amenable to analysis with some
statistical tools that were illustrated
• Yes, you may need to synthesize all the parts but that comes
later in the course
Thank you
The lecture recording will be made
available in your Moodle course site.
学霸联盟
