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Wallace, Introduction to Information Systems, 3rd
edition
Instructor’s Manual
Chapter 7, Business Intelligence and Decision Making
Copyright © 2018 Pearson Education, Inc.
1
Chapter 7
Business Intelligence and Decision Making
Learning Objectives
1. Define business intelligence, and describe the three levels of decision making that
it supports.
2. Describe the major sources of business intelligence, and provide examples of their
usefulness.
3. Explain several approaches to data mining and decision support that help
managers analyze patterns, trends, and relationships, and make better data-driven
decisions.
4. Explain how digital analytics are used as a source of business intelligence and
why they are so valuable for understanding customers.
5. Describe how dashboards, portals, and mashups help visualize business
intelligence, and explain the role that the human element plays in business
intelligence initiatives.
Solutions to Chapter Review Questions
7-1. How do you define business intelligence?
Business intelligence describes the vast quantities of information that an
organization might use for data-driven decision making.
7-2. What are the three levels of decision making that business intelligence
supports?
Business intelligence supports decision making at operational, tactical, and
strategic organizational levels.
7-3. What are the most important sources of business intelligence inside the
organization? What makes them useful?
The major sources of business intelligence are the organizations that own data
repositories and data from external sources. Internal sources include transactional
databases and data warehouses. The company’s own databases are a source of
data about customers, employees, suppliers, and financial transactions. An

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2
internal transaction system is the source of data to produce a summarized sales
report by region and product.
7-4. What are some examples of external sources of business intelligence?
External sources include data from websites, blogs, wikis and social networks, as
well as publicly accessible and purchased databases. An external database is a
source for demographics, educational levels, income, ethnicity, housing, and
employment information. Information from an external database is useful for
developing targeted marketing campaigns. A publicly accessible website is a
source of useful business intelligence such as competitive pricing.
7-5. How can managers use data mining techniques to analyze patterns, trends,
and relationships? How does this lead to better data-driven decision making?
Approaches to data mining depend on the kind of data and the needs of the user.
Online analytical processing (OLAP) systems allow users to interact with a data
warehouse and perform “slice and dice” analyses to reveal patterns and trends.
Using OLAP, retail managers analyze sales transaction by customer gender and
age group, and find relationships that can guide marketing campaigns. Statistical
and modeling techniques are also used to identify patterns and trends. Market
basket analysis is a type of statistical analysis used by retailers to decide where to
place products in a store. Text mining is an approach to analyzing unstructured
text information such as customer comments. What-if analysis, goal seeking, and
optimizing are Excel spreadsheet data analysis techniques that support decision-
making by enabling the user to build models that establish relationships between
variables. Forecasting tools are used to predict tomorrow’s demand or next
month’s sales by analyzing historical data and seasonal trends.
7-6. What is text mining?
Text mining is a variation of data mining in which unstructured text information
is the source of business intelligence, rather than structured data.
7-7. What are examples of statistical techniques that managers can use to
simulate business situations, optimize variables, and forecast sales or other
figures?
What-if analysis builds a model that establishes relationships between many
variables and then changes some of the variables to see how the others are
affected. In goal seeking, instead of estimating several variables and calculating
the result, the user sets a target value for a particular metric and tells the program
which variable to change to try to reach the goal. An extension of goal seeking is
optimization, in which the user can change many variables to reach some
maximum or minimum target, as long as the changes stay within some constraints
identified by the user. Forecasting tools analyze historical or seasonal trends and

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3
then take into account existing and predicted business conditions to estimate some
variable of interest.
7-8. What are examples of applications that draw on artificial intelligence for
decision support?
Applications that draw on artificial intelligence include robotics, expert systems,
and neural nets. Service robots are appearing in business, government, and other
sectors. An expert system mimics the reasoning of a human expert, drawing from
a base of knowledge about a particular subject area to come to a decision or
recommendation. Neural networks attempt to mimic the way the human brain
works, and are widely used where massive data sets are available.
7-9. How are digital analytics used to assess the effectiveness of websites?
Web analytics describes the practice of measuring, collecting, and analyzing
website clickstream data to produce business intelligence. Website metrics
include visitors, unique visitors, average time spent on the site, new visitors, depth
of visit, languages, traffic sources, and service providers. Web-content related
metrics include page views, bounce rate, top landing pages, and top exit pages.
There are additional measures specifically for social media activities and e-
commerce activities. All of these metrics are a rich source of business intelligence
for understanding customers because they track every single click by every visitor
to an organization’s website. Each measure reveals something a little different
that can help reveal how people are interacting with the site, and how well the site
is meeting the goals set for it.
7-10. How do dashboards, portals, and mashups support decision making?
Dashboards, portals, and mashups are graphical user interfaces that organize and
summarize information vital to the user’s role and the decisions that users make.
Dashboards summarize key performance indicators. Portals are gateways that
provide access to a variety of relevant information from many different sources on
one screen. Mashups are gateways that aggregate content from multiple internal
and external sources.
7-11. How does the human element affect decision making?
Humans are the critical element in decision making, deciding what intelligence to
rely upon, what tools to use, and how to interpret the results. Humans are also
subject to cognitive biases that may lead to poor decisions.

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Solutions to Projects and Discussion Questions
7-12. Why do organizations use external data as a source of business intelligence?
What are examples of sources of external data? How might retail giant
Walmart use external data to make tactical-level decisions? How might its
decision makers use external data to make strategic-level decisions?
External databases that are either purchased or are publicly accessible are
excellent candidates as business intelligence sources. For example, The U.S.
Census Bureau maintains many searchable databases with information about
demographics, educational levels, income, ethnicity, housing, and employment.
Student answers will vary on how an organization may use external data at the
tactical and strategic level. Walmart may purchase new vehicle registration data to
consider which automotive products to stock in stores. Walmart may use publicly
available census data to consider where to locate one of the hundreds of small
stores that it plans to open in the next three years.
7-13. How can an intelligent agent assist with a term paper? Visit your university’s
library home page to locate the “Search Databases” feature. If your library
offers the “ABI/INFORM” database, choose that and enter several keywords
(for example, “social media in organizations”) into the Basic Search dialog
box. (If your library does not offer ABI/INFORM, try doing this exercise on
a different database.) Review the results, then select “Refine Search” to select
additional databases and/or specify additional search criteria. When you
have the results you want, select the “Set Up Alert” option to schedule an
alert. Prepare a brief report that describes the alert options that are
available for your search. How frequently can you receive updates? How
long can you receive updates? Are there options other than frequency and
duration? Would you recommend this intelligent agent to other students
working on term papers?
If a university’s library does not have the “ABI/Inform” database, the student can
use a comparable database such as “Business Source Complete.” Answers may
vary, as the exercise is designed to require the student to interact with an online
database to set up a search alert. One point that could be discussed is the schedule
for an alert, which may be daily, monthly, weekly, or every three months. Another
scheduling option determines the duration of the search. which may be as brief as
two weeks or as long as one year.
7-14. First Class Salons maintains a company website to promote its chain of 12
regional health salons. The website includes links to information about its
locations, special offers, and FAQs about its services, as well as “About Us”
and “Contact Us” links. How can First Class Salons use information from its
website to gain business intelligence? Consider the various visitor-related and
content-related web metrics and suggest at least six specific metrics that First

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5
Class Salons would want to analyze. Prepare a brief report of your
suggestions.
Answers will vary, as the object of this exercise is to prompt the student to apply
the concept of web analytics to a business situation. An example is to track the
number of visitors to the websites.
7-15. The Springfield Family Community Center has an outdoor pool that
operates May through October. The director is interested to learn if the
Community Center can afford the $57,000 cost to install a pool-covering
dome so that patrons may swim year-round. It will also cost about $200 a
month for power to keep the dome inflated for 6 months each year. How can
the director use forecasting to evaluate the likelihood of selling sufficient
tickets to pay for this improvement? Prepare a brief report to the director
that explains forecasting. Be sure to include suggestions on both internal and
external data that would be useful for this analysis.
Forecasting tools usually analyze historical and seasonal trends, and then take into
account existing and predicted business conditions to estimate some variable of
interest. Using internal historical data and external weather report data, the
Community Center could analyze the correlation between weekly temperature and
sales revenue from pool tickets to forecast ticket sales on a year-round basis.
7-16. Digital dashboards began to appear in the 1990s as organizations looked for
ways to consolidate and display data to make it accessible and useful for busy
executives. Visit www.digitaldashboard.org or www.dashboardsby
example.com or search the Internet to learn more about digital dashboards.
What is the relationship between digital dashboards and key performance
indicators? Work in a small group with classmates to consider how a digital
dashboard can be used by a Radio Shack or other electronics store manager.
What specific daily performance indicators would he or she want to see on a
digital dashboard? What design tips would you offer to the dashboard
developer? As a group, create a hand-drawn sketch of a dashboard design
for the Radio Shack manager.
A dashboard should summarize key performance indicators (KPI). Answers will
vary. This question is designed to require students to incorporate concepts and
information introduced in this chapter to prepare answers. Sample answers could
include (a) daily sales volume by product line and/or (b) sales volume by day of
the week.

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6
Solutions to Application Exercises
7-17. Excel Application: Analyzing Revenue and Expenses for City Hospital
Seminars
Figure 7-25 shows the Excel spreadsheet that Bora uses to evaluate the variables
relating to the hospital seminar series. She has asked you to use Excel to create a
similar spreadsheet to conduct additional what-if and goal seek analyses. You will
need to use the following formulas:
Figure 7-25
The hospital seminar series data.
Revenue:
Registration Fees = Attendees per seminar ×
Registration fee × Seminars per year
Parking Fees = (Attendees per seminar /
Average number attendees per car) ×
Seminars per year × Parking fee
Expenses:
Speakers’ Fees = Speaker’s fee per session ×
Seminars per year
Tech support = Tech support cost per
session × Seminars per year
Marketing = Marketing cost per seminar ×
Seminars per year
Room rental = Room rental per seminar ×
Seminars per year
What-If Questions
1. What would be the impact on net profit if the average attendance per
seminar increased to 45?
Profit will increase to $6,180 if average attendance per seminar is increased to 45.
2. What would be the impact on net profit if the average attendance dropped to
35?
Profit will decrease to $1,740 if average attendance dropped to 35.

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7
3. What would be the impact on net profit if the parking fee is reduced to $3?
Profit will decrease to $3,576 if the parking fee is reduced to $3.
4. What would be the impact on net profit if the speaker’s fee increased to $550
per seminar?
Profit will decrease to $3,360 if the speaker’s fee is increased to $550 per
seminar.
5. What would be the impact on net profit of increasing the marketing expense
per seminar to $350, resulting in an increase in average attendance per
seminar to 50?
Profit will increase to $7,200 if marketing expenses increase to $350 per seminar
and attendance increases to 50 attendees per seminar.
6. What would be the impact on net profit of an increase in room rental per
seminar to $300?
Profit will decrease to $3,360 if room rental per seminar increases to $300.
7. If Bora can negotiate a room rental fee of $160 per seminar, how much will
net profit increase?
Profit will increase to $5,040 if the room rental fee is decreased to $160 per
seminar.
8. If technical support is included in the room rental per seminar, what is net
profit?
Profit will increase to $5,760 if technical support is included in the room rental
per seminar.
Goal Seek Questions
1. Given the expenses and variables presented in the figure, how many
attendees per seminar are required to generate a net profit of $5,500?
Given the expenses and variables as presented, it requires 43 attendees per
seminar to generate net profit of $5,500.
2. What parking fee results in a net profit of $4,150?
A parking fee of $600 results in a net profit of $4,150.

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8
3. What registration fee per attendee results in a net profit of $5,750?
A registration fee of $39 per attendee results in a net profit of $5,750.
7-18. Access Application: Marketing City Hospital Seminars
Download the City Hospital database, Ch07Ex02. Write a query that sorts
registrants by the type of seminar they have attended. Include the session date as
well as attendee information. Modify the query to identify registrants who attended
a Knee Replacement seminar. Use the report wizard to create a report that lists the
session dates and the names and phone numbers of those who have attended Knee
Replacement seminars. This report serves as a “patient contact sheet” that hospital
staff will use to call previous attendees to invite them to attend the new seminar.
How many patients are listed on the report? Review the attendees table. Is there
additional patient information the hospital could collect that may be useful for
future marketing campaigns?
Students should download the Access database named Ch07Ex02.accdb and create a
query that sorts registrants by seminar type. The query should include the session date
and the attendee information. Students should modify the query to list only the
individuals who attended a Knee Replacement seminar, and use the query to create a
“Patient Contact” report that lists 12 patients. Answers will vary regarding other types of
information that may be useful for future marketing campaigns. Suggestions may include
attendee email address or referral information.

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9
Solutions to Case Study Questions
Case 1— Cracking Fraud with Government’s Big Data
7-19. What are some ways that data mining could be used to detect fraud in health
insurance claims?
The purpose of this question is to help students think about some of the
underlying logic and business rules in data mining. For example, analysts could
look for patterns such as:
• Doctor’s office submits claims for services that exceed the capacity of that
doctor’s office to deliver services
• Claims are submitted for services provided to a non-valid social security
number
• Claims for the same service to the same individual at the same time are
submitted by multiple doctor’s offices
7-20. How could private insurance companies and public government agencies
collaborate to combat insurance fraud?
The purpose of this question is to help students think about common objectives
across organizations, and the manner in which IS can enable collaboration across
organizations. It could certainly be expected that criminals conducting insurance
fraud would target both private insurance companies and public government
agencies at the same time. In order for private insurance companies and public
government agencies to collaborate, the organizations will need to share
information with each other. This information could uncover additional patterns
across organizations beyond what either organization could uncover on its own.
For example, it might be possible to discover that claims are being simultaneously
submitted to multiple organizations for the same service to the same individual at
the same time.
7-21.What types of business skills would be necessary to define the rules for and
analyze the results from data mining?
The purpose of this question is to help students understand the skills and human
capital that accompany the use of IS in business applications. In this case, in
addition to understanding the data mining application itself, analysts would also
need to understand the healthcare industry, insurance industry, and basic
principles of law investigation and enforcement. From a personal skills
standpoint, students would need the ability to collaborate as part of a team on the
investigation.

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10
7-22. What business processes are necessary to complement the IS component of
data mining?
Continuing from question 3 above, students need to understand the business
processes that accompany IS applications, in addition to the business skills and
business domains that accompany IS applications. In this case, the data mining
application will only be effective if it is accompanied by other processes to reduce
insurance fraud. For example, the insurance firm must have a collections
mechanism to recover funds that have already been paid, an enforcement
mechanism (or the ability to collaborate with law enforcement) so that offenders
are punished, and an advisory mechanism to consult with other members of the
healthcare value chain (such as doctor’s offices and hospitals) to preempt fraud.
Case 2— TV and Twitter: How Nielsen Rates Programs with “Social TV”
7-23. What potential value does Nielsen intend to add to their ratings by data
mining Twitter to analyze social TV patterns?
The focus of this case study is to draw the students’ attention to the business use
of data mining social media to detect patterns, trends, and relationships to enhance
traditional network ratings. Nielsen adds value to their traditional set-top ratings
by providing the networks with reports on programs based upon data from
Twitter. This analysis enables Nielsen to identify which family members are
viewing programs and provides insights into their attitudes. While these analytical
reports do not replace Nielsen’s core ratings basis, the set-top box, they do
augment those ratings for the networks. Business intelligence is used to expand
the scope of the data sources into social media, and this enriches the value of their
services to the networks.
7-24. What are the drawbacks of using Twitter as a rating tool? Do these
disadvantages compromise the value of the Nielsen ratings?
This question helps students understand how bias in the data sampling effects the
results and can change the value to the networks. Two biases influence the reports
from Twitter analysis. First, not all viewers use Twitter, and further those viewers
who do use it may over-populate the sample with their opinions. Secondly, the
age bias of those who use Twitter means the sample does not necessarily
represent the whole audience. These sample biases can be overcome, however, by
making note of them and by correlating the Twitter findings with the traditional
results. Because Nielsen does this and does not discontinue their use of set-top
boxes for sampling, the value of the results is not compromised but enhanced. The
important idea is that sampling bias can be accounted and overcome if the results
are interpreted properly.

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11
7-25. How might the use of Twitter and other social media be influencing the
viewing habits of the American audience?
Twitter may be influencing the results gathered from the traditional set-tops.
Twitter users could be creating a TV buzz about a program, which in turn gets
more people to turn on the program and join the social TV experience. This is
evidenced by the high correlation between traditionally collected data and Twitter
analysis as a program reaches the end of its season. The key point is that when
analyzing two different samples, as in this case, the analysts need to be alert for
the relationships between the samples and how one may be changing the other.
Analysis of this relationship could have added value for the networks, especially
if data mining identifies which programs and patterns drive the relationship and
behavioral changes.
7-26. If Nielsen extended their data mining of social media to include Facebook as
well as Twitter, what differences might they expect in the audience being analyzed?
Would this analysis have any value to the networks? Why or why not?
The point of this question is to draw attention to the importance of sample
selection to business intelligence analysis. The number of Facebook users is much
greater than the number of Twitter users, which means that this audience would
be larger. Further the demographics for Facebook users include a broader age
distribution than Twitter. Another valuable difference between the two sites is that
Facebook users must provide more biographical details than Twitter users. Data
mining Facebook samples could produce more relationships and patterns because
of this additional profile data. Other differences exist between these two social
media, and when analyzing both, these differences need to be noted and studied.
With the addition of more data from Facebook, Nielsen potentially has the
opportunity for even more pattern recognition and correlations with TV programs
and ads. However much value this added data offers, analysts would have the
challenge of managing and accounting three samples and three sets of biases.
Effort and expense increase as the number of different samples expands. The
value to the networks would be in receiving more meaningful and reliable viewer
analysis, but this value would have to be greater than Nielsen’s investment for it
to be viable.
Solutions to E-Project Questions
E-Project 1—Detecting Suspicious Activity in Insurance Claims
Detecting unusual patterns in drug prescriptions is the focus of this e-project. To
begin, download the Excel file called Ch07_MedicalCharges. The worksheet
contains columns showing a sample of hypothetical prescription drug claims over a
period of years.

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12
7-27. Create a pivot table and chart to show the total amounts paid by year for this
pharmacy, by dragging Year to the Axis Fields (Categories) box and Amount to the
Values box. Be sure you are looking at the sum of Amounts in your chart. Which
year had the highest sales for prescription drugs?
The year 2009 has the highest sales ($1,894) for prescription drugs.
7-28. Change the pivot table to show total sales by month by removing Year from
the Axis Fields and dragging Month to that box. During which month of the year
does this pharmacy tend to sell the most prescription drugs?
This pharmacy tends to sell the most prescription drugs ($1,200) during October.

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7-29. Remove Month and put Prescriber ID in the Axis Field box. Which prescriber
generates the most income for this pharmacy?
Prescriber 52 generates the most income ($2,888) for this pharmacy.

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14
7-30. Remove PrescriberID and put PatientID in the Axis field box. Which patient
generates the most income for the pharmacy?
Patient 21201 generates the most income ($7,490) for the pharmacy.
7-31. Let’s take a closer look at this patient by filtering the records. Click on
PatientID in the PivotTable Field List and uncheck all boxes except for this patient.
Drag Year under PatientID in the Axis Fields box so you can see how this person’s
spending patterns have changed. Which year shows the most spending?
Patient 21201 spent the most ($1,720) during 2008.

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15
7-32. Let’s see who is prescribing for this patient. Remove Year from the Axis Fields
box and drag PrescriberID to the box. Which Prescriber has the highest spending
total?
Prescriber 217 has prescribed the most ($2,020) for patient 21201.

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7-33. Now let’s see what is being prescribed. Drag DrugName to the Axis Field box
under Prescriber ID. What might you conclude from this chart?
This table and chart below shows that patient 21201 is receiving prescriptions for
Vicodin from many different prescribers. Further investigation may show that
patient 21201 is receiving duplicate prescriptions for the same medication during
the same timeframe, and that patient 21201 may be taking more than the
recommended amount of this drug.

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E-Project 2—Analyzing Nielsen TV Ratings with Excel
In this e-project, you will explore TV ratings and analyze them with Excel.
Download the Excel file called Ch07_NielsenRatings. This file contains ratings for
popular network programs for two separate weeks in 2013.
(http://www.nielsen.com/us/en/top10s.html). The rating represents the percent of
U.S. households that were watching that channel at the time (of those whose TV was
turned on).
7-34. Calculate three new columns.
a. Percent change (up or down) in number of viewers from the July 4
data to the July 11 data for each show.
b. Percent change (up or down) in rating for each show.
c. Absolute change in the number of viewers for each show.
The spreadsheet will look like this.
7-35. Answer the following questions:
a. Which show gains the largest number of viewers from July 4 to July 11?
America’s Got Talent - Wed
b. Which show is the biggest loser from July 4 to July 11, in terms of change
in ratings?

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America’s Got Talent - Tue
c. Compute the total viewers for these shows for July 4 to July 11. How many
total viewers watched one of the TV shows in this list during the week of July
4?
50,533,000
d. What is the percent change in total viewers for the shows in this list from
July 4 to July 11?
Total viewership for these shows increased by 6.3%.
is the biggest loser from March 25 to April 1, in terms of change in ratings?

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Edwards F. 110 Norfolk terrace, Bayswater
Edwards E. 3 Norland square, Notting hill
Edwards E. 23 Orsett terrace, Hyde park
Edwards T. 7 Park cottages, Hammersmith
Edwards Mrs, 23 St. Ann’s villas, Notting hill
Edwards W. 32 St. George’s road, Notting hill
Edwards Mrs, 31 Upper Phillimore place, Kensington
Edwards Miss, 10 Alexandra villas, Uxbridge road, Shepherd’s bush
Edwards Mrs, 17 Victoria grove, Kensington
Egerton Col. 44 Norfolk square, Hyde park
Egg C. 22 Chepstow villas west, Bayswater
Egg G. 39 Great Western terrace, Westbourne park
Eggbrecht Miss, 15 Paddington green
Eglese Capt. R.A. 3 Buckingham terrace, Notting hill
Egley W. M. 59 Hereford road, Bayswater
Eglington T. 18 Orchard street, Kensington
Eiffe T. 14 Westmoreland road, Bayswater
Eland G. F. 28 Warwick gardens, Kensington

Elborough Miss, Clifton villa, New road, Hammersmith
Elbrecht Mrs, 15 Westbourne park crescent, Paddington
Elborough T. 2 Apsley villas, New road, Hammersmith
Elcock R. 19 Eastbourne terrace, Paddington
Elder W. 19 Richmond road, Shepherd’s bush
Elder J. 18 Royal crescent, Notting hill
Elderfield T. 8 King street east, Hammersmith
Elderstrath J. 22 Denbigh terrace, Bayswater
Eldridge Mrs, 9 Woodfield terrace, Westbourne grove, Paddington
Elearn Rev. W. H. 28 Delamere terrace, Paddington
Elford W. 96 Star street, Paddington
Elias N. 64 Inverness terrace, Kensington gardens
Elias H. 18 Prince’s gardens, Kensington
Elisha C. 3 Stranraer place, Maida vale
Ellaby E. K. 48 Priory road, Kilburn
Ellard Mrs, 28 Lanark villas, Maida vale
Ellard Mrs, 145 Ledbury road, Bayswater
Ellaway J. 5 Cambridge terrace, Paddington
Ellers G. 17 Gloucester terrace, Hyde park
Ellerton Mrs, 1 Aldridge road villas, Westbourne park
Ellerton J. 3 Aldridge road villas, Westbourne park
Ellerton J. L. 6 Connaught place, Hyde park

Ellery Mrs, 10 Leinster terrace, Bayswater
Ellicombe R. R. 6 Hyde park gate south
Elliott J. 14 Alfred road, Paddington
Elliott Mrs, 4 Arundel gardens, Kensington park
Elliott E. 2 St. Peter’s terrace, Hammersmith
Elliott J. 11 Crompton street, Paddington
Elliott E. Stanmore cottage, Edgware road, Kilburn
Elliott Sir W. K.C.B. 20 Cambridge square, Hyde park
Elliott H. & Miss, 31 Cambridge square, Hyde park
Elliott Mrs, 7 Canterbury villas, Maida vale
Elliott R. 34 Cirencester street, Paddington
Elliott H. C. 2 Elgin road, Notting hill
Elliott W. 2 Foxley road, Kensington
Elliott G. L. 7 Hyde park gate south
Elliott W. H. 21 Lancaster gate, Hyde park
Elliott C. 34 Phillimore gardens, Kensington
Elliott Lady, 7 Stanhope street, Hyde park
Elliott Major, 10 The terrace, Bayswater
Elliott Mrs, 26 Upper Phillimore place, Kensington
Ellis J. 10 Addison road, Kensington
Ellis S. 19 Campden grove, Kensington
Ellis C. 11 Carlton terrace, Notting hill

Ellis W. R. 23 Carlton terrace, Notting hill
Ellis E. 15 Johnson street, Notting hill
Ellis R. 2 Lansdowne crescent, Notting hill
Ellis A. 16 Lower Phillimore place, Kensington
Ellis F. 28 Norfolk square, Hyde park
Ellis Mrs, 12 Oxford road, Kilburn
Ellis S. 50 Oxford terrace, Hyde park
Ellis S. 11 Randolph road, Maida hill
Ellis J. 2 Sale street, Paddington
Ellis Mrs, 25 Upper Southwick street, Paddington
Ellis Mrs, 29 Warwick road, Paddington
Ellis T. Hawthorn cottage, Wood lane, Shepherd’s bush
Ellison C. 10 Prince of Wales’s terrace, Kensington
Elliston Miss, 2 Addison crescent, Kensington
Elliston A. 28 Maida hill west
Ellyatt F. 5 Edmund terrace, Kensington park
Elmore R. 3 Colville road, Bayswater
Elnor J. 3 Sussex place, Hammersmith
Elphinstone Dr. S. 62 Hereford road, Bayswater
Elsey T. 6 Francis street, Paddington
Elsey Mrs, 12 Kildare terrace, Westbourne park
Elstonear E. 19 Convent gardens, Kensington park

Elsworth J. 23 St. Peter’s square, Hammersmith
Elton Mrs, 11 Clarendon gardens, Maida hill
Elton E. 46 Edwards place, Kensington
Elton Miss 17 Tavistock terrace, Westbourne park
Elverson J. H. 6 Carlton road, Kilburn
Elwell Mrs, 1 Alfred cottages, North end, Hammersmith
Elwin J. 23 Pembroke square, Ken.
EIwood Mrs, 83 Hereford road, Bayswater
Ely Marchioness of, 9 Princes gate, Kensington
Ely G. 1 Prince’s cottages, St. Peter’s road, Hammersmith
Emanuel Mrs, 193 Lansdowne road, Notting hill
Emanuel L. 6 Talbot terrace, Westbourne park
Embleton H. 4 Pembroke terrace, Kensington
Emerson J. Brunswick gardens, Kensington
Emery J. 20 Cuthbert street, Paddington
Emery J. 2 Richmond road, Shepherd’s bush
Emery J. 1 St. George’s road, Notting hill
Emes Miss E. 13 Connaught square, Hyde park
Emlyn W. O. 35 Inverness road, Bayswater
Emmens T. H. 4 Lancaster road, Notting hill
Emmett G. N. 2 Kensington park gardens, Kensington park
Emmett W. H. 3 St. George’s terrace, South Kensington

Emms Mrs, 6 Portland place, North end, Hammersmith
Emnorfopoulo S. G. 12 Stanley gardens, Kensington park
Empedocles P. 23 Queensborough terrace, Bayswater
Empson H. 45 Kensington park gardens, Kensington park
Emsley J. A. 25 Norfolk square, Hyde park
Enderson Mrs, 3 Sutherland place, Bayswater
Engel C. 54 Addison road, Kensington
Engel E. 24 Pembroke road, Kensington
England T. H. 82 Addison road, Kensington
England W. 7 St. James square, Notting hill
Engleback E. L. 46 Phillimore gardens, Kensington
English R. 31 Elm grove, Hammersmith
English C. 14 Ladbroke villas, Notting hill
English C. 25 St. George’s road, Notting hill
Ensor J. 15 Park villas, Hammersmith
Enthoven H. J. 103 Westbourne terrace, Hyde park
Enticknap Miss, 20 & 21 Earl’s terrace, Kensington
Entwistle G. J. 13 Church street, Paddington
Entwistle W. H. 6 Pembroke road, Kilburn
Epron A. 4 Statham street, Paddington
Eraser W. 17 Radnor place, Hyde park
Erick —, 29 Pembroke road, Kensington

Erck Mrs, 14 Stanley terrace, Kensington park
Erain G. 45 Westbourne park villas, Westbourne park
Erle G. 16 Cambridge road, Hammersmith
Erle Sir W. 12 Prince’s gardens, South Kensington
Errington E. 15 Abingdon villas, Kensington
Errinshaw G. 3 Victoria terrace, Notting hill
Erskim C. 8 Kensington gardens square, Bayswater
Erskine W. 33 Craven hill gardens, Bayswater
Erskine T. 13 Oxford road, Kilburn
Erskine Col. G. 22 Westbourne park, Paddington
Erswell Mrs, 20 St. Mary’s terrace, Paddington
Escott T. 36 Westbourne park road, Bayswater
Escudier F. S. Ada cottage, Albion road, Hammersmith
Escudier S. 46 Leinster square, Bayswater
Esden J. H. 24 Andover place, Kilburn
Eskell A. 49 Portsdown road, Maida hill
Essam W. 22 Senior street, Harrow road
Etherington C. Denham lodge, Hammersmith road
Etlenger A. 94 Portsdown road, Maida hill
Eustace H. 1 Uxbridge street, Notting hill
Evans W. 3 Alfred road, Paddington
Evans D. 11 Alpha place west, Kilburn

Evans G. 1 Arundel gardens, Kensington park
Evans T. 4 Beavor lane, Hammersmith
Evans A. 37 Blenheim crescent, Notting hill
Evans R. 11 Cottage road, Paddington
Evans E. 52 Denmark road, Kilburn
Evans Mrs, 20 Cambridge street, Edgware road
Evans Mrs, Campden hill road, Kensington
Evans J. W. 12 Carlisle terrace, Kensington
Evans W. 68 Clarendon street, Paddington
Evans Rev. T. 24 Colville road, Bayswater
Evans R. 15 Gloucester place, Hyde park
Evans Miss, 3 Ladbroke place west, Notting hill
Evans G. 8 Victoria terrace, Notting hill
Evans G. A. 10 Ladbroke villas, Notting hill
Evans G. H. E. 21 Lansdowne road, Notting hill
Evans J. 6 Park villas, Hammersmith
Evans A. 8 Pickering place, Bayswater
Evans Mrs, 1a Porchester terrace, Bayswater
Evans Capt. 10 Portland road, Notting hill
Evans G. 28 Portland road, Notting hill
Evans J. 40 Queen’s road, Bayswater
Evans W. 2 St. Agnes’ villas, Bayswater road

Evans W. F. 7 St. Alban’s road, Kensington
Evans L. 29 St. George’s road, Notting hill
Evans J. 33 St. Mark’s crescent, Notting hill
Evans Miss, 12 Sheffield terrace, Kensington
Evans A. G. 1 Stamford Brook cottages, Hammersmith
Evans —, 19 Westbury road, Paddington
Evans Mrs, 55 Westbourne terrace, Hyde park
Evans J. L. 120 Westbourne terrace, Hyde park
Evans G. 33 Westbourne park, Paddington
Everest Rev. R. 50 Cleveland square, Bayswater
Everest H. 1a Monmouth road, Bayswater
Everest Col. Sir G. K.C.B., F.R.S., F.R.G.S., F.R.A., 10 Westbourne street,
Hyde park
Everest H. 14 Westbourne terrace road, Paddington
Everett E. 26 Elm grove, Hammersmith
Everett —, 44 St. James’s square, Notting hill
Evers A. 52 Cornwall road, Westbourne park
Every W. 11 Colville square, Bayswater
Evett G. 6 Cambridge terrace, Kensington
Evill D. A. 18 Brondesbury villas, Kilburn
Evill E. 40 Cambridge road, Kilburn
Evill T. L. 27 Elgin crescent, Kensington park

Ewart Mrs, 6 Alexandra terrace, Hammersmith
Ewart Col. D. 50 Lancaster gate, Hyde park
Ewart Col. C.B. 17 Norfolk square, Hyde park
Ewart J. 25 Sussex square, Hyde park
Ewart W. M.P. 6 Cambridge square, Hyde park
Ewbank C. 5 Hereford road, Bayswater
Ewen G. W. 7 Prince’s square, Bayswater
Ewens Capt. 14 Porteus road, Paddington
Ewin W. 27 Delamere crescent, Paddington
Ewings A. 10 Chichester place, Paddington
Exon Miss, 2 Sussex place, Kensington
Exton Miss, 4 Argyll terrace, Kensington
Eykyn T. 8 Lansdowne terrace, Notting hill
Eyles Mrs, 3 Alpha place, Kilburn
Eyles Mrs, 8 Cary villas, Hammersmith
Eyre J. G. 21 Durham terrace, Westbourne park
Eyre G. 1 Carlton terrace, Kilburn
Eyre Mrs, 48 Norfolk square, Hyde park
Eyre G. P. L. 60 Prince’s square, Bayswater
Eyre Miss, 44 Queen’s road, Bayswater
Eyre W. Glanvill lodge, Silchester road, Notting hill
Eyston R. 1 Elvaston place, South Kensington

F.
Faed T. Sussex villa, Campden hill, Kensington
Fagan Mrs, 18 Carlisle terrace, Kensington
Fagg T. 6 Alexandra street, Westbourne park
Fairbairn W. A. 9 Holland park, Notting hill
Fairbairn T. 23 Queen’s gate, South Kensington
Fairbairn Mrs, Dresden house, Hammersmith
Fairbanks Miss, 31 Westmoreland place, Bayswater
Faires G. 9 Ladbroke crescent, Notting hill
Fairhead Mrs, 42 Blomfield road, Maida hill
Fairman Mrs, 4 Blomfield crescent, Paddington
Fairman Capt. A. F. R.N. 39 Pembroke square, Kensington
Fairman Mrs, 63 Princes gate, South Kensington
Fairland Mrs, 6 Inkerman terrace, Kensington
Fairlough Miss, 9 Warrington terrace, Maida hill
Faithful J. R. 12 Chichester road villas, Kilburn
Faithful —, 10 Connaught square, Hyde park
Falconer L. J. 44 Connaught square, Hyde park
Falkland Viscount, C. H. 4 Princes gate, South Kensington
Falkner G. 3 Marlborough terrace, Paddington
Falks J. Warwick lodge, Shepherd’s bush
Fall J. 3 Bridge avenue, Hammersmith

Fane C. 35 Connaught square, Hyde park
Fane W. D. 7 Norfolk crescent, Hyde park
Fanning Mrs, D. Gloucester gardens, Bayswater
Fardell T. G. 30 Oxford square, Hyde park
Farebrother Miss, 7 Lansdowne terrace, Kensington
Farenden Mrs, 8 Lancaster road, Notting hill
Farlar W. 11 Grove terrace, The Grove, Hammersmith
Farlow J. Marsh villa, Hammersmith
Farman S. York cottage, Hammersmith
Farnell J. 1 St. Agnes villas, Shepherd’s bush
Farow A. 28 Leamington road villas, Westbourne park
Farquhar Miss, 39 Bark place, Bayswater
Farquhar T. H. 20 Upper Phillimore gardens, Kensington
Farquharson A. 10 Blomfield street, Maida hill
Farr G. 5 Great Western crescent, Westbourne park
Farr T. 3 St. Catherine’s villas, Hammersmith
Farra J. 37 Westbourne park road, Westbourne park
Farrance Mrs, 16 Kensington gardens square, Bayswater
Farrance Mrs, 6 Ladbroke terrace, Notting hill
Farrance J. 14 Westbourne park
Farrant G. 14 Kildare gardens, Westbourne park
Farrell J. 18 St. Peter’s square, Hammersmith

Fallen W. 60 Westbourne park villas, Westbourne park
Farrington Lady, 8 Queensborough terrace, Bayswater
Farrow A. 7 Pickering place, Bayswater
Farrow R. 122 Queen’s road, Bayswater
Farrow J. 4 Victoria grove, Bayswater
Farrow T. R. 34 Westbourne park road, Westbourne park
Faulkner J. Cathnor villa, Hammersmith
Faulkner Miss 7 Queen street, Hammersmith
Faulkner Mrs, 2 Sussex gardens, Paddington
Faulkner F. 4 Warwick crescent, Kensington
Favarger Rene H. 38 Arundel gardens, Kensington park
Favaux Miss, 13 Brondesbury road, Kilburn
Favour S. 27 Chichester road villas, Kilburn
Fawcett Mrs, 26 Norfolk terrace, Bayswater
Fawcett W. T. 11 Westbourne street, Hyde park
Fawssett W. 34 Sussex place, Kensington
Fearon Mrs, 33 Addison gardens south, Kensington
Fearon C. A. 90 Westbourne terrace, Paddington
Featherstone G. 7 Abingdon villas west, Kensington
Featherstonhaugh R. T. 9 Flora villas, Hammersmith
Feaver Miss, 18 Cambridge street, Paddington
Feaver T. 22 Warwick road, Paddington

Feetham T. O. 23 Arundel gardens, Kensington park
Feilde M. H. 24 Queen’s road, Notting hill
Felgate W. 44 Gloucester crescent, Hyde park
Felix —, 17 Askew road, Shepherd’s bush
Fell J. 5 Princes road, Notting hill
Fellowes H. D. 78 Cambridge terrace, Paddington
Fellows J. G. 19 Caves terrace, Hammersmith
Fellows Capt. W. 30 Portsdown road, Maida hill
Felton W. J. 26 Kensington park gardens, Kensington park
Fenn C. Cromer cottage, Kilburn
Fenn R. L. 32 Victoria road, Kensington
Fennah T. 9 Ledbury road, Bayswater
Fendall Mrs, M. A. 27 Princes square, Bayswater
Fennell E. 17 Blomfield terrace, Paddington
Fennely R. 6 Aldridge road villas, Westbourne park
Fenner E. 6 Caves terrace, Hammersmith
Fenning Mrs, 9 Cambridge terrace, Kensington
Fennings A. 15 St. Ann’s road north, Notting hill
Fenoulhet Miss, 16 Kensington crescent, Kensington
Fenton F. 26 King street east, Hammersmith
Fenton Misses, 13 Norland place, Notting hill
Fenton W. 32 Richmond road, Bayswater

Fenwick Mrs, 8 Wellington terrace, Hammersmith
Feress Mrs, 8 Victoria grove, Kensington
Ferguson D. 1 Brondesbury villas, Kilburn
Ferguson H. 2 Charles street, Kensington
Ferguson J. 23 Devonshire terrace, Bayswater
Ferguson J. 5 Elgin terrace, Kensington park
Ferguson Mrs, 3 Serampore terrace, Hammersmith road
Fergusson Mrs, 6 Stamford villas, Kensington
Ferry B. 42 Inverness terrace, Kensington gardens
Ferrie P. 44 Kensington park gardens, Kensington park
Ferrier J. 6 Somers place, Hyde park
Fervalger C. J. 6 Essex villas, Kensington
Fesser A. H. The Terrace, Bayswater
Fesser J. N. 98 Westbourne terrace, Paddington
Feucster W. 17 Clarendon street, Harrow road
Feversham Mrs, M. 1 Ladbroke villas, Notting hill
Few Mrs, 7 The Terrace, Kilburn
Fewster T. 2 Hammersmith terrace, Hammersmith
Fewster F. 18 Moscow road, Bayswater
Ffennell W. J. 31 Arundel gardens, Kensington park
Ficklin S. 23 Northumberland place, Bayswater
Field Mrs, 13 Abingdon villas, Kensington

Field Mrs, 25 Caroline place, Bayswater
Field W. 18 Gloucester gardens, Bayswater
Field Miss, 7 Lansdowne crescent, Notting hill
Field J. Portland place, North end, Hammersmith
Field O. 43 Sussex gardens, Paddington
Field T. 10 Sussex terrace, Hyde park
Field J. L. 5 Percy villas, Kensington
Fielder H. 20 Carlton villas, Maida vale
Fielding A. 12 Ladbroke gardens, Notting hill
Fielding Hon Col. 29 Princes gate, South Kensington
Fife Mrs, 45 Leamington road villas, Westbourne park
Figes G. 11 Charles street, Kensington
Filby R. 36 Alpha terrace, Kilburn
Fildesley E. 4 Blomfield road, Maida hill
Filmes J. 16 Delamere street, Paddington
Filpott W. 1 Sheffield terrace, Shepherd’s bush
Finch Mrs, 16 Askew road, Shepherd’s bush
Finch Mrs, 9 Denbigh terrace, Bayswater
Finch G. 5 Devonshire place, Paddington
Finch G. 8 Elgin terrace, Maida vale
Finch J. 28 Stratheden terrace, Hammersmith
Finch C. M. D. 58 Porchester terrace, Bayswater

Finch J. 8 Victoria gardens, Notting hill
Fincham Mrs, 31 Bedford gardens, Kensington
Finden G. C. 21 Talbot square, Hyde park
Findlay Mrs, 23 Campbell street, Paddington
Finlay Mrs, 4 Talbot square, Hyde park
Finlayson W. F. 12 Campden hill road, Kensington
Finlayson J. F. 8 Kilburn Priory
Finlayson J. 15 Lansdowne crescent, Notting hill
Finmore Mrs, 7 Upper Porchester street, Hyde park
Find Mrs, 36 Oxford terrace, Paddington
Finn T. 24 Pembridge villas, Bayswater
Finney S. 34 Cambridge square, Hyde park
Finney Major, 9 Godolphin road, Hammersmith
Finney W. 3 Lancaster street, Hyde park
Finnie R. B. 4 Vale place, Hammersmith road
Finnis Mrs, 12 Park villas, Hammersmith
Firebrace Mrs, 22 Queen’s gardens, Hyde park
Firmage Miss, 11 Cuthbert street, Paddington
Firman P. 13 Ladbroke square, Notting hill
Firman P. S. 17 Ladbroke square, Notting hill
Firmin H. 39 Priory road, Kilburn
Fischel M. M. 10 Garway road, Bayswater

Fishell F. S. 11 Southwick place, Hyde park
Fishbourne Capt. E. R.N. 6 Delamere terrace, Paddington
Fisher Mrs, 18 Argyll road, Kensington
Fisher B. J. 6 Blenheim terrace, Notting hill
Fisher R. 8 Bramley road villas, Notting hill
Fisher W. 19 Cambridge square, Hyde park
Fisher T. S. 57 Chepstow place, Bayswater
Fisher T. S. 24 Douglas place, Bayswater
Fisher W. S. 14 Durham terrace, Westbourne park
Fisher J. H. 12 Elgin crescent, Notting hill
Fisher Mrs H. 5 Inkerman terrace, Kensington
Fisher R. 72 Kensington gardens square, Bayswater
Fisher R. G. 14 Priory road, Kilburn
Fisher Mrs, 1 Maida hill
Fisher E. 108 Norfolk terrace, Bayswater
Fisher C. 38 Oxford road, Kilburn
Fisher R. 17 Pembroke square, Kensington
Fisher Mrs, 3 Stanhope street, Hyde park.
Fisher Mrs, 50 Talbot terrace, Westbourne park
Fisher Mrs, 160 Westbourne terrace, Paddington
Fiske W. G. 160 Anglesea villas, Hammersmith
Fitch Mrs, Bath cottage, Hammersmith

Fitzball E. White cottage, Hammersmith road
Fitzgerald J. 2 Bath cottages, Notting hill
Fitzgerald Mrs, 19 Cambridge street, Paddington
Fitzgerald F. G. 24 Campden grove, Kensington
Fitzgerald T. 14 Cuthbert street, Paddington
Fitzhugh W. 11 Arundel gardens, Kensington park
Fitzhugh Miss, 1 St. Stephen’s square, Westbourne park
Fitzjohn I. 62 Albert road, Kilburn
Fitzpatrick J. 6 Arundel gardens, Kensington park
Fitzroy —, 2 Grove terrace, The Grove, Hammersmith
Fitzroy Lord E. L. 6 Prince’s gardens, South Kensington
Fitzsimmons Mrs, 10 Colville terrace west, Bayswater
Fitzwater J. 3 St. Peter’s terrace, Hammersmith
Fitz-Wygram Lady, 10 Connaught place, Hyde park
Fitz-Wygram Sir R. Bart. 10 Connaught place, Hyde park
Flack Mrs, 12 Gold Hawk terrace, Hammersmith
Flanagan Mrs, 4 Caroline terrace, Hammersmith
Flavell T. W. 10 Queen’s gardens, Hyde park
Flavell H. 14 St. Stephen’s road, Westbourne park
Fleming T. W. 51 Lancaster gate, Hyde park
Fleming Mrs, 61a Portsdown road, Maida hill
Fleming J. 26 Queen’s gate, South Kensington

Flemmich J. F. 10 Westbourne terrace, Paddington
Flenen R. W. 49 Queen’s road, Notting hill
Fletcher W. 2 Kensington gardens terrace, Hyde park
Fletcher C. W. 8 Notting hill sq
Fletcher Mrs, 10 Pembridge place, Bayswater
Fletcher Mrs, 6 Pembroke square, Kensington
Fletcher W. 6 Richmond terrace, Bayswater
Fletcher W. G. 17 Russell road, Kensington
Fletcher Miss, 12 Sheffield gardens, Kensington
Fletcher Mrs, 1 Wellington terrace, Hammersmith
Fletcher J. D. 12 Westbourne terrace, Paddington
Fleury J. 44 Queen’s gardens, Hyde park
Flinn W. 8 Park place, Kensington
Flint J. 34 Arundel gardens, Kensington park
Flood J. 23 St. Mary’s terrace, Paddington
Florence S. 3 Alexander road, Kilburn
Floricine —, 58 Cambridge street, Paddington
Floutow M. 49 Porchester terrace, Bayswater
Flower E. C. 36 Chichester road villas, Kilburn
Flowers J. 16 Chepstow villas west, Bayswater
Flowers M. 14 Norfolk crescent, Hyde park
Floyer Rev. C. 81 Inverness terrace, Bayswater

Foley Miss, 1 Inverness place, Bayswater
Folkard J. Priory cottage, Lower mall, Hammersmith
Follett B. S. Q.C. 15 Cambridge square, Hyde park
Fonblanque W. 45 Connaught square, Hyde park
Foord Miss, 12 Cornwall road, Hammersmith
Foot T. 14 Silchester road, Notting hill
Foot Mrs, 12 Westbourne terrace road, Paddington
Foot G. E. 5 Gordon cottages, Hammersmith
Foote J. 6 Abingdon villas west, Kensington
Foote —, 8 Devonport road, Hammersmith
Foote Rev. J. A. 13 Fulham place, Paddington
Forbes C. 77 Addison road, Kensington
Forbes J. 2 Adelaide terrace, Shepherd’s bush
Forbes N. W. 5 Carlton villas, Maida vale
Forbes D. E. 1 Carton place, Westbourne park
Forbes —, 9 Devonport street, Hyde park
Forbes A. 2 Dudley place, Paddington
Forbes Miss, 11 Essex villas, Kensington
Forbes Mrs, 48 Inverness terrace, Kensington gardens
Forbes Mrs, 11 Kildare gardens, Bayswater
Forbes J. 15 Pembridge villas, Bayswater
Forbes J. S. 45 Phillimore gardens, Kensington

Forbes S. 24 Phillimore gardens, Kensington
Forbes G. 42 Portsdown road, Maida hill
Forbes C. 28 Queen’s gate terrace, South Kensington
Forbes Col. F. 3 Victoria road, Kensington
Force E. 82 Ledbury road, Bayswater
Force Miss, 26 Pembroke square, Kensington
Ford T. K. 11 Cambridge road, Kilburn
Ford Mrs, T. 5 Clarendon gardens, Maida hill
Ford F. 1 Fieldings cottages, Hammersmith
Ford W. 13 Kensington park villas, Kensington park
Ford J. J. Park side, Hammersmith
Ford Mrs, 22 St. Ann’s road, Notting hill
Ford G. 16 St. Mark’s crescent, Notting hill
Ford J. M. 1 St. Peter’s road, Hammersmith
Forder C. 41 Cambridge terrace, Hyde park
Fordham J. 27 Alpha place north, Kilburn
Fordham T. 14 Westmoreland place, Bayswater
Foreman Miss, 28 Oxford terrace, Paddington
Foreman J. 12 Westbourne villas, Paddington
Forest Rev. L. 62 Leamington road villas, Westbourne park
Forlonge Mrs, 29 Gloucester place, Hyde park
Forman H. 49 Clarendon street, Harrow road

Forrest J. 24 Westbourne park villas, Westbourne park
Forster J. 3 Flora villas, Hammersmith
Forster J. 6 Clarendon place, Hyde park square
Forster W. 29 Fulham place, Paddington
Forster T. 17 Verulum terrace, Hammersmith
Forster J. 11 Hanover terrace villas, Notting hill
Forster Miss, 96 Hereford road, Bayswater
Forsyth Miss, 8 Blomfield street, Maida hill
Forsyth A. 5 Charlotte terrace, Hammersmith
Forsythe W. A. 37 Gloucester place, Hyde park
Fort R. M. P. 24 Queen’s gate gardens, South Kensington
Fortescue Mrs, 24 Westbourne park terrace, Paddington
Fortune T. Monmouth house, Notting hill
Forty J. 2 Blenheim villas, Hammersmith
Fosbrooke Miss, 28 Sutherland place, Bayswater
Foskett G. 33 St. Luke’s road villas, Westbourne park
Fossett T. 13 Waterloo street, Hammersmith
Foster C. B. 4 Addison terrace, Notting hill
Foster Mrs, 18 Bridge avenue, Hammersmith
Foster D. 22 Carlton villas, Maida vale
Foster E. 15 Chepstow place, Bayswater
Foster Lieut.-Gen. 5 Cleveland terrace, Hyde park

Foster Mrs, 12 Garway road, Bayswater
Foster T. C. 7 Orsett terrace, Hyde park
Foster R. 11 Pembroke square, Kensington
Foster Mrs, 11 St. George’s road, Notting hill
Foster T. M. 2a Sheffield terrace, Kensington
Foster J. L. 5 Southwick place, Hyde park
Foster W. 1 St. Stephen’s terrace, Shepherd’s bush
Foster Mrs, 10 Warwick crescent, Paddington
Fothergill Lieut.-Col. S. 8 Inverness road, Bayswater
Fotheringham R. 5 Tavistock terrace, Westbourne park
Foulkes W. 7a Maida hill west
Fowler Miss, 2 York villas, Hammersmith
Fowler J. 56 Alfred road, Paddington
Fowler R. 58 Kensington gardens square, Bayswater
Fowler C. 34 Ladbroke square, Notting hill
Fowler G. 70 Ledbury road, Bare
Fowler Mrs, 36 Northumberland place, Bayswater
Fowler C. 8 Pembridge villas, Bayswater
Fowler O. 2 Pembroke villas, Kilburn
Fowley A. 11 Queen’s gate, South Kensington
Fox Gen. C. R. 1 Addison road, Kensington
Fox Mrs, C. 1 Addison road, Kensington

Fox J. 14 Campden hill road, Kensington
Fox J. 8 Dawson place, Bayswater
Fox G. 21 Gloucester place, Hyde park
Fox J. 6 Pembridge crescent, Notting hill
Fox H. H. 68 Queen’s gardens, Hyde park
Fox Col. A. L. 10 Upper Phillimore gardens, Kensington
Fox Mrs, 21 Warwick crescent, Kensington
Foy W. H. 18 Kensington gate, South Kensington
Foyle J. 140 King street west, Hammersmith
Fozard W. 43 Pembroke road, Kilburn
Framagee 24 Devonshire terrace, Bayswater
Frampton DeKeue, 82 Oxford terrace, Paddington
France Mrs, 8 Lansdowne terrace, Paddington
Frame J. 2 Norfolk terrace, Bayswater
Francis E. 1 Albert street, Paddington
Francis J. 1 Alfred row, Shepherd’s bush
Francis C. S. 14 Blomfield road, Maida hill
Francis Miss, 5 Albion place, Hammersmith
Francis W. 29 Burlington road, Westbourne park
Francis Mrs, 2 Cambridge street, Paddington
Francis C. 11 Chapel street, Hammersmith
Francis T. H. 7 Lancaster road, Notting hill

Francis W. 19 Victoria street, Paddington
Francis F. G. 14 Warwick crescent, Paddington
Franghiadi S. 129 Westbourne terrace, Paddington
Franklin R. 11 Alexander road, Kilburn
Franklin Mrs, 30 Blomfield street, Paddington
Franklin Lady, 2 Kensington gore upper, Kensington
Franklin E. A. 2 Leinster terrace, Hyde park
Franklin J. 8 Malvern terrace, Kilburn
Franklin J. W. 14 Senior street, Paddington
Franklin Mrs, 5 Sunderland terrace, Bayswater
Franks G. 7 Canterbury terrace, Kilburn
Franz Dr. A. 67 Oxford terrace, Paddington
Fraser H. 29 Arundel gardens, Kensington park
Fraser Mrs, 30 Gloucester terrace, Hyde park
Fraser G. S. 21 Great Western terrace, Westbourne park
Fraser J. W. 8b Kensington Palace gardens, Kensington
Fraser Mrs, 6 Kildare terrace, Westbourne park
Fraser G. 2 Wilby terrace, Notting hill
Fraser C. 15 Lancaster gate, Hyde park
Fraser H. 51 Richmond road, Bayswater
Fraser J. 6 Royal crescent, Notting hill
Fraser Mrs, 1 St. Mary’s road, Westbourne park

Fraser E. 1 Percy villas, Kensington
Frearson Mrs, 44 Westbourne park road, Westbourne park
Frederick J. S. 11 Abingdon villas, Kensington
Freeman Capt. 13 Blomfield street, Paddington
Freeman C. 1 Buckingham villas, Notting hill
Freeman Mrs, 1 Canterbury villas, Maida vale
Freeman W. E. Alfred cottage, Kilburn
Freeman Mrs, 18 Howley place, Paddington
Freeman C. 1 Buckingham villas, Notting hill
Freeman H. 8 Monmouth road, Bayswater
Freeman J. 6 Grove cottages, North End, Hammersmith
Freeman W. H. 145 Queen’s road, Bayswater
Freeman J. 15 Radnor place, Hyde park
Freeman R. S. 12 St. James’s square, Notting hill
Freeman Mrs, 3 Silchester road villas, Notting hill
Freeman R. 12 Upper Phillimore place, Kensington
Freeman C. E. 1 Warwick crescent, Paddington
Freeth Mrs, 48 Burlington road, Westbourne park
Freeth Col. 2 Essex villas, Kensington
French Rev. M. D. M.A. 22 Albion street, Paddington
French Mrs, 4 Convent gardens, Kensington park
French J. 15 Elgin crescent, Kensington park

French J. 12 Elgin terrace, Kilburn
French Miss, 32 Oxford road, Kilburn
French F. 6 Silchester road villas, Notting hill
French Mrs, Blenheim house, Middle Mall, Hammersmith
Frere J. H. 30 Cambridge terrace, Paddington
Frew Capt. A. T. Rifle cottage, Kilburn
Frewin Mrs, 64 St. Mary’s terrace, Paddington
Friend R. R. 1 Apsley villas, Hammersmith
Friend J. B. 30 Sussex square, Hyde park
Fripps Mrs, 19 Carlton terrace, Paddington
Frith W. P. R.A. 7 Pembridge villas, Bayswater
Fröembling Dr. O. 26 Richmond road, Bayswater
Froom P. C. 4 Cambridge square, Hyde park
Froom H. 10 Courtland place, Kensington
Froom W. 100 Gloucester terrace, Hyde park
Froom A. 2a Portsdown road north, Maida vale
Froom Mrs E. A. 16 Princes square, Bayswater
Frossell W. 25 London street, Paddington
Frost R. 84 Bridge road, Hammersmith
Frost C. 5 Brondesbury road, Kilburn
Frost Mrs, 17 Clifton villas, Maida hill
Frost J. 15 Cumberland terrace, Westbourne park

Frost J. 21 Kildare gardens, Westbourne park
Frost C. M. 47 Ladbroke square, Notting hill
Frost Rev. G. 28 & 29 Kensington square, Kensington
Frost M. 122 Norfolk terrace, Bayswater
Frost —, 19 Pembroke square, Kensington
Froy W. Grove lodge, The Grove, Hammersmith
Frust H. J. 26 Portsdown road, Maida hill
Fry T. H. 5 Arundel gardens, Kensington park
Fry R. 4 Brondesbury terrace, Kilburn
Fry A. 1 Holland villas road, Kensington
Fry Rev. H. D.D. 28 Kensington gardens square, Bayswater
Fry Mrs, 10 Orchard street, Kensington
Fryer Mrs, 36 Albion street, Paddington
Fryer Mrs, 23 Cambridge road, Kilburn
Fryer Mrs, 31 Delamere, crescent, Paddington
Fryer C. 13 Lanark villas, Maida vale
Fryer Col. H. T. 21 St. Petersburgh place, Bayswater
Fuller Mrs & Miss, 34 Addison road, Kensington
Fuller G. 26 Argyll road, Kensington
Fuller J. T. 1 Blenheim terrace, Notting hill
Fuller F. J. 238 Maida vale
Fuller Mrs, 24 Northumberland place, Bayswater

Fuller T. 22 Pembridge villas, Bayswater
Fullerton R. T. 18 Westbourne square, Paddington
Fullimore W. 64 Cambridge terrace, Paddington
Furdato J. 1 Tewkesbury villas, Shepherd’s bush
Furlonger C. J. 75 Gloucester terrace, Hyde park
Furrell E. W. 13 Bedford gardens, Kensington
Fursdon G. 6 Westbourne park
Furtado Mrs, 20 Addison road, Kensington
Fustin J. 32 Caves terrace, Hammersmith
Futcher Mrs, 37 Cambridge terrace, Paddington
G.
Gabrielli J. 4 Cleveland terrace gardens, Kensington
Gabrielli A. 21 Queen’s gate terrace, South Kensington
Gadsden —, 8 Park villas, Hammersmith
Gadsdon J. 6 Westbury terrace, Paddington
Gahagan Miss, 40 Elgin road, Notting hill
Gaisford Miss, 14 Connaught terrace, Paddington
Gaisford H. 25 Woodfield place, Paddington
Gaitskell Rev. J. M.A. 1 Church street, Kensington
Gaitskill A. 3 Queens gardens, Bayswater
Gale R. 7 Portland place, Hammersmith

Gale E. J. M. 2 Sheffield gardens, Kensington
Gale Mrs, Mall house, Water side, Hammersmith
Gallaway Mrs J. A. 66 Gloucester crescent, Paddington
Galloway R. H. 27 Hyde park square
Galloway Miss, 67 Princes square, Bayswater
Galloway J. 20 Salusbury terrace, Kilburn
Gallsworthy E. 78 Elgin crescent, Notting hill
Galsworthy E. T. 8 Kensington gore lower, Kensington
Galton Miss, 7 Canterbury terrace, Kilburn
Gamgee J. 28 Queen’s road, Bayswater
Gandell S. 22 Palace gardens villas, Kensington
Gane E. 2 Cathnor villas, Hammersmith
Gape Mrs, 2 Abingdon villas west, Kensington
Garde J. E. 14 Princes road, Notting hill
Garden F. F. 3 Loudoun villas, Hammersmith
Garden Miss, 9 Queensborough terrace, Bayswater
Garden Mrs, 100 Gloucester terrace, Paddington
Gardener W. 10 Albert terrace, Notting hill
Gardener Mrs, 21 Cambridge square, Hyde park
Gardener C. 2 Charles street, Paddington
Gardener S. 64 Cirencester street, Harrow road
Gardener W. 15 Elgin terrace, Maida vale

Gardener D. 27 Orsett terrace, Hyde park
Gardener D. 12 Warwick terrace, Maida hill
Gardiner W. 6 Rosedale cottages, Hammersmith
Gardiner Mrs, 27a Canterbury road, Kilburn
Gardiner R. 33 Gloucester gardens, Paddington
Gardiner J. 39 Norland square, Notting hill
Gardiner Mrs, 12 Pembridge villas, Bayswater
Gardiner J. 20 Westbourne terrace, Hyde park
Gardner W. H. 80 Gloucester terrace, Paddington
Gardner P. 41 Inverness terrace, Bayswater
Gardner Mrs, 3 Kensington gardens square, Bayswater
Gardner J. 10 Kildare gardens, Westbourne park
Gardner Mrs, 4 Avenue terrace, Hammersmith
Gardner Mrs, 20 Notting hill square, Notting hill
Gardner J. 3 St. James terrace, Paddington
Gardner Mrs, 14 St. Mark’s crescent, Notting hill
Gardner H. 1 Westbourne terrace, Hyde park
Gardner H. 2 Westbourne terrace villas, Paddington
Garland J. 1 Clarendon place, Notting hill
Garland E. W. 15 Queen’s gate, South Kensington
Garlick G. 9 Albion villas, Hammersmith
Garlick W. 11 Albion villas, Hammersmith

Garling H. B. 6 St. Agnes villas, Bayswater road
Garman —, 1 Bath cottages, Notting hill
Garner Mrs, 39 Cirencester street, Harrow road
Garnett Mrs, 4 Argyll road, Kensington
Garnett Mrs, 6 Brunswick terrace, Kensington
Garnett Mrs, 17 Caves terrace, Hammersmith
Garnier Lady C. 4 Leinster gardens, Bayswater
Garoocock Mrs, 12 Askew road, Shepherd’s bush
Garrett C. 19 Amberley road, Maida hill
Garratt J. 7 Pine Apple place, Maida vale
Garrett F. B. 3 St. Mary Abbott’s terrace, Kensington road
Garrett Miss, 7 Westbourne park road, Bayswater
Garrique Mrs, 2 Albert place, Kensington
Garrod W. 4 Bark place, Bayswater
Garrould J. 8 Senior street, Harrow road, Paddington
Garwood W. R. 12 Cary villas, Hammersmith
Garwood Rev. J. M.A. 2 Upton villas, Kilburn
Gaselee Serj. 2 Cambridge square, Hyde park
Gaselee Mrs, 106 Gloucester terrace, Paddington
Gaseltine Miss, 12 Fulham place, Maida hill
Gaskell M. 2 Montague place, Hammersmith road west
Gaskoin G. 3 Westbourne park, Paddington

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