SOLUTIONS MANUAL
Data Visualization: Exploring and Explaining with Data, 2nd
Edition
By Jeffrey D. Camm, James J. Cochran
SC
O
R
EG
U
ID
ES
,TABLE OF CONTENT
Chapter 1. Introduction
Chapter 2. Selecting a Chart Type
Chapter 3. Data Visualization and Design
SC
Chapter 4. Purposeful Use of Color
Chapter 5. Visualizing Variability
O
Chapter 6. Exploring Data Visually
R
Chapter 7. Explaining Visually to Influence with Data
EG
Chapter 8. Data Dashboards
Chapter 9. Telling the Truth with Data Visualization
U
ID
ES
, Solution and Answer Guide: Camm/Cochran/Fry/Ohlmann, Data Visualization - Exploring and Explaining with Data, 2nd
Edition, © 2025, 9780357631348; Multi-chapter Cases
Solution and Answer Guide
CAMM/COCHRAN/FRY/OHLMANN, DATA VISUALIZATION - EXPLORING AND EXPLAINING WITH
DATA, 2ND EDITION, © 2025, 9780357631348; M ULTI-CHAPTER CASES
SC
TABLE OF CONTENTS
Case Problem 1: Piedmont General Hospital....................................................................1
Case Problem 2: Bayani Bulb Bases....................................................................................6
Case Problem 3: International Monetary Fund Housing Affordability.......................13
O
Case Problem 4: Short Term Rental Market....................................................................16
R
CASE PROBLEM 1: PIEDMONT GENERAL HOSPITAL
EG
Piedmont General Hospital (PGH) is a large healthcare facility located in the southeastern United
States, renowned for its commitment to quality care of its patients. Megan Avery is the Vice President
of Quality for PGH. Megan manages the team responsible for implementing and overseeing all quality
and risk control programs.
PGH has recently noticed an uptick in the readmission rate. A readmission is when a discharged
patient returns to the hospital for the same or related care within 30 days of being discharged
U
from the hospital. Readmissions strain resources and negatively impact patient outcomes and
patient satisfaction. Furthermore, for patients on Medicare, the Hospital Readmissions Reduction
Program passed by Congress financially penalizes hospitals by reducing the amount paid for
ID
services when the hospital exceeds their expected number of readmissions. PGH has, to date,
never exceeded its expected number of readmissions, and Megan wants to ensure that PGH
continues to have fewer readmissions than expected.
Megan has asked her team to investigate factors that lead to higher readmission rates. Megan
ES
believes that if factors that seem related to higher readmission rates can be identified from the
analysis of patient records, the team will be able to create strategies to mitigate the number of
readmissions. To that end, David Moore, a data analyst on Megan’s team, has collected a sample of
500 records for patients admitted for congestive heart disease.1 The data set in the file
readmissions includes the following variables:
Patient ID Number – patient identification numbers from 1 to 500 to protect the privacy of
the patient
1
The data in this case are simulated.
© 2025 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 1
accessible website, in whole or in part.
, Solution and Answer Guide: Camm/Cochran/Fry/Ohlmann, Data Visualization - Exploring and Explaining with Data, 2nd
Edition, © 2025, 9780357631348; Multi-chapter Cases
Age – the age of the patient at the time of the original admission to the hospital
Comorbidities – health conditions other than congestive heart failure present in the
patient
Length of Stay – duration of the stay in the hospital in the original admission
Discharge Instructions – instructions given to the patient when discharged (exercise
and/or medication)
SC
Compliance – how well the patient complied with the discharge instructions (scored as
low, medium, or high)
Readmission status – whether or not the patient was readmitted for congestive heart
failure within 30 days of discharge (1 = yes and 0 = no)
Now, David needs to analyze the data and develop a report for Megan. LO 2.1, 2.2, 2.3,
O
2.4, 6.3, 6.5
SUMMARY REPORT
David’s goal is to develop a report that shows which factors (if any) seem to be related to patient
R
readmission. Your job is to conduct the analysis and help David with the report. Begin by
investigating each of the following relationships with an appropriate chart:
1. How is readmission rate related to age? Consider age as a categorical variable with
EG
categories 40–49, 50–59, 60–69, and 70–80, and construct a column chart to answer this
question.
2. How is readmission rate related to length of stay?
3. Is readmission rate higher for certain comorbidities?
4. How is readmission rate related to compliance?
5. Construct a clustered column chart with readmission rates for the comorbidities by age
U
category.
Based on your analysis, what are the characteristics of patients that should be targeted first for
interventions and/or special follow-up care?
ID
Your report should include the charts and tables generated to answer the questions posed and a
brief description and conclusion of each.
Solution:
If we convert age from a continuous variable to categories of ranges of ages, all of the variables
ES
except length of stay can be viewed as categorical. Hence, we will rely on column charts to
explore the 500 patient records. We begin by answering each posed question, and then we will
provide a recommendation.
© 2025 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 2
accessible website, in whole or in part.
Data Visualization: Exploring and Explaining with Data, 2nd
Edition
By Jeffrey D. Camm, James J. Cochran
SC
O
R
EG
U
ID
ES
,TABLE OF CONTENT
Chapter 1. Introduction
Chapter 2. Selecting a Chart Type
Chapter 3. Data Visualization and Design
SC
Chapter 4. Purposeful Use of Color
Chapter 5. Visualizing Variability
O
Chapter 6. Exploring Data Visually
R
Chapter 7. Explaining Visually to Influence with Data
EG
Chapter 8. Data Dashboards
Chapter 9. Telling the Truth with Data Visualization
U
ID
ES
, Solution and Answer Guide: Camm/Cochran/Fry/Ohlmann, Data Visualization - Exploring and Explaining with Data, 2nd
Edition, © 2025, 9780357631348; Multi-chapter Cases
Solution and Answer Guide
CAMM/COCHRAN/FRY/OHLMANN, DATA VISUALIZATION - EXPLORING AND EXPLAINING WITH
DATA, 2ND EDITION, © 2025, 9780357631348; M ULTI-CHAPTER CASES
SC
TABLE OF CONTENTS
Case Problem 1: Piedmont General Hospital....................................................................1
Case Problem 2: Bayani Bulb Bases....................................................................................6
Case Problem 3: International Monetary Fund Housing Affordability.......................13
O
Case Problem 4: Short Term Rental Market....................................................................16
R
CASE PROBLEM 1: PIEDMONT GENERAL HOSPITAL
EG
Piedmont General Hospital (PGH) is a large healthcare facility located in the southeastern United
States, renowned for its commitment to quality care of its patients. Megan Avery is the Vice President
of Quality for PGH. Megan manages the team responsible for implementing and overseeing all quality
and risk control programs.
PGH has recently noticed an uptick in the readmission rate. A readmission is when a discharged
patient returns to the hospital for the same or related care within 30 days of being discharged
U
from the hospital. Readmissions strain resources and negatively impact patient outcomes and
patient satisfaction. Furthermore, for patients on Medicare, the Hospital Readmissions Reduction
Program passed by Congress financially penalizes hospitals by reducing the amount paid for
ID
services when the hospital exceeds their expected number of readmissions. PGH has, to date,
never exceeded its expected number of readmissions, and Megan wants to ensure that PGH
continues to have fewer readmissions than expected.
Megan has asked her team to investigate factors that lead to higher readmission rates. Megan
ES
believes that if factors that seem related to higher readmission rates can be identified from the
analysis of patient records, the team will be able to create strategies to mitigate the number of
readmissions. To that end, David Moore, a data analyst on Megan’s team, has collected a sample of
500 records for patients admitted for congestive heart disease.1 The data set in the file
readmissions includes the following variables:
Patient ID Number – patient identification numbers from 1 to 500 to protect the privacy of
the patient
1
The data in this case are simulated.
© 2025 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 1
accessible website, in whole or in part.
, Solution and Answer Guide: Camm/Cochran/Fry/Ohlmann, Data Visualization - Exploring and Explaining with Data, 2nd
Edition, © 2025, 9780357631348; Multi-chapter Cases
Age – the age of the patient at the time of the original admission to the hospital
Comorbidities – health conditions other than congestive heart failure present in the
patient
Length of Stay – duration of the stay in the hospital in the original admission
Discharge Instructions – instructions given to the patient when discharged (exercise
and/or medication)
SC
Compliance – how well the patient complied with the discharge instructions (scored as
low, medium, or high)
Readmission status – whether or not the patient was readmitted for congestive heart
failure within 30 days of discharge (1 = yes and 0 = no)
Now, David needs to analyze the data and develop a report for Megan. LO 2.1, 2.2, 2.3,
O
2.4, 6.3, 6.5
SUMMARY REPORT
David’s goal is to develop a report that shows which factors (if any) seem to be related to patient
R
readmission. Your job is to conduct the analysis and help David with the report. Begin by
investigating each of the following relationships with an appropriate chart:
1. How is readmission rate related to age? Consider age as a categorical variable with
EG
categories 40–49, 50–59, 60–69, and 70–80, and construct a column chart to answer this
question.
2. How is readmission rate related to length of stay?
3. Is readmission rate higher for certain comorbidities?
4. How is readmission rate related to compliance?
5. Construct a clustered column chart with readmission rates for the comorbidities by age
U
category.
Based on your analysis, what are the characteristics of patients that should be targeted first for
interventions and/or special follow-up care?
ID
Your report should include the charts and tables generated to answer the questions posed and a
brief description and conclusion of each.
Solution:
If we convert age from a continuous variable to categories of ranges of ages, all of the variables
ES
except length of stay can be viewed as categorical. Hence, we will rely on column charts to
explore the 500 patient records. We begin by answering each posed question, and then we will
provide a recommendation.
© 2025 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 2
accessible website, in whole or in part.
