,ACCESS Test Bank for Introduction to Data Analytics for Accounting 2nd Edition
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
Richardson
Richardson, Terrell, Teeter – Introduction to Data Analytics for Accounting, 2nd Edition – Chapter 1
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws W s Ws Ws Ws
Chapter 1 End-of-Chapter Assignment Solutions
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Multiple Choice Questions
Ws Ws
1. (LO 1-2) Which is the lowest level of critical thinking skills in Bloom’s Taxonomy?
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
a. Create
b. Remember
c. Apply
d. Analyze
2. (LO 1-2) Which is the highest level of critical thinking skills in Bloom’s Taxonomy?
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
a. Create
b. Apply
c. Analyze
d. Understand
3. (LO 1- Ws
2) Which is the appropriate ordering of critical thinking skills in Bloom’s Taxon
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
omy, where the “>” symbol means higher order skills?
Ws Ws Ws Ws Ws Ws Ws Ws
a. Remember > Apply Ws Ws
b. Apply > Analyze Ws Ws
c. Analyze > Evaluate Ws Ws
d. Create > Analyze Ws Ws
4. (LO 1-3) Which step of the AMPS model most appropriately addresses the axiom,
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
“Your data won’t speak unless you ask it the right data analytics questions”?
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
a. Ask the Question Ws Ws
b. Master the Data Ws Ws
c. Perform the Analysis Ws Ws
d. Share the Story Ws Ws
5. (LO 1- Ws
3) Which step of the AMPS model most appropriately addresses the question of the
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
best way to communicate data analytics findings with a decision maker?
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
a. Ask the Question Ws Ws
b. Master the Data Ws Ws
c. Perform the Analysis Ws Ws
d. Share the Story Ws Ws
6. (LO 1- Ws
3) What type of question is predicting whether a company will go bankrupt in the co
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ming two years? Ws Ws
a. What happened? What is happening? Ws Ws Ws Ws
b. Why did it happen? What are the root causes of past results? Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
c. Will it happen in the future? What is the probability something will
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
happen? Is it forecastable? Ws Ws Ws
© McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill L
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
LC.
1-1
mynursytest.store
,ACCESS Test Bank for Introduction to Data Analytics for Accounting 2nd Edition
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
Richardson
Richardson, Terrell, Teeter – Introduction to Data Analytics for Accounting, 2nd Edition – Chapter 1
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws W s Ws Ws Ws
d. What should we do based on what we expect will happen? How do we Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
optimize our performance based on potential constraints?” Ws Ws Ws Ws Ws Ws
7. (LO 1- Ws
3) What type of question is choosing to take certain tax deductions based on the w
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ay managers believe tax legislation will change in the near future?
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
a. What happened? What is happening? Ws Ws Ws Ws
b. Why did it happen? What are the root causes of past results?
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
c. Will it happen in the future? What is the probability something will happen? I
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
s it forecastable? Ws Ws
d. What should we do based on what we expect will happen? How do we Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
optimize our performance based on potential constraints?” Ws Ws Ws Ws Ws Ws
8. (LO 1- Ws
3) What type of question is finding the detail to more clearly understand why net in
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
come is decreasing when revenues are increasing?
Ws Ws Ws Ws Ws Ws
a. What happened? What is happening? Ws Ws Ws Ws
b. Why did it happen? What are the root causes of past results?Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
c. Will it happen in the future? What is the probability something will happen? I
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
s it forecastable? Ws Ws
d. What should we do based on what we expect will happen? How do we Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
optimize our performance based on potential constraints?” Ws Ws Ws Ws Ws Ws
9. (LO 1- Ws
4) What visualization type is most appropriate for evaluating the relationships betwe
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
en values?Ws
a. Bar chart Ws
b. Pie chart Ws
c. Histogram
d. Scatterplot
10. (LO 1-5) Which of the following software tools specialize in data visualizations?
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
a. SPSS and Power Query Ws Ws Ws
b. Alteryx and Tableau Prep Ws Ws Ws
c. Power BI and Tableau Ws Ws Ws
d. R and Python Ws Ws
Discussion Questions Ws
1. (LO 1- Ws
1) The computer is better at automated, repetitive tasks since it can be pro
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
grammed. The computer is also not subject to fatigue and can process ma W s Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ssive amounts of data easier than a human can. Most of the value-
Ws Ws Ws Ws Ws Ws Ws Ws W s Ws Ws Ws
added tasks and higher order thinking skills, such as analyzing, evaluating a
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
nd creating, are performed better by human accountants because they are
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws W
not easily programmed by a set of fixed rules. The ability
s Ws Ws Ws Ws Ws Ws Ws Ws W s Ws
© McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LL
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
C.
1-2
mynursytest.store
, ACCESS Test Bank for Introduction to Data Analytics for Accounting 2nd Edition
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
Richardson
Richardson, Terrell, Teeter – Introduction to Data Analytics for Accounting, 2nd Edition – Chapter 1
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws W s Ws Ws Ws
to recognize tradeoffs, evaluating alternatives, and evaluating ad hoc facts are
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
all better performed by humans.
Ws Ws Ws Ws Ws
2. (LO 1- Ws
2) The skills taught in the introduction to financial accounting were the lo
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
wer order thinking skills (noted in Bloom’s Taxonomy) such as rememberi
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ng, understanding and applying. Application of journal entries, computing
Ws Ws Ws W s Ws Ws Ws Ws Ws
trial balances, recording transactions, bank reconciliation, etc. are all exa
Ws Ws Ws Ws Ws Ws Ws Ws Ws
mples of lower order skills. Ws Ws Ws Ws
3. (LO 1- Ws
3) Accountants understand the tradeoffs between relevant data and reliabl
Ws Ws Ws Ws Ws Ws Ws Ws Ws
e data (such as that data which might exhibit more representational faithfu
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
lness).
Accountants also understand the tradeoffs between unstructured and struct Ws Ws Ws Ws Ws Ws Ws Ws
ured data, data internal or external to the company, and even the potential co
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
st of acquiring and processing the data as compared to the potential value pr
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ovided by use of the data. Ws Ws Ws Ws Ws
4. (LO 1- Ws
3) Mastering the data includes accessing, cleaning, and transforming the da
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ta to prepare the data for analysis.
Ws Ws Ws Ws Ws Ws
5. (LO 1- Ws
3) Data analytics might be viewed as successively peeling the layer of an oni
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
on. By peeling the first layer of the onion, you now are able to see the next la
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
yer and evaluate it and remove it to get to the third layer, etc. Often times,
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws W s Ws Ws
the AMPS model must be performed multiple times, refining the question (A
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
sk the Question), possibly considering different types of data (Master the Da
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ta), performing additional analysis (Perform the Analysis) and retelling the st
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ory in each iteration (Sharing the Story) before the issue/problem/challenge
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
can be finally addressed with some confidence.
Ws Ws Ws Ws Ws Ws
6. (LO 1- Ws
3) Descriptive analysis reports what happened. Generally, evaluating the re
Ws Ws Ws Ws Ws W s Ws Ws Ws
venues and earnings performance starts with descriptive analysis and contin
Ws Ws Ws Ws Ws Ws Ws Ws Ws
ues with diagnostic analysis to understand “Why it happened?”.
Ws Ws Ws Ws Ws Ws Ws Ws
7. (LO 1-4, LO 1- Ws Ws Ws
5) Data acquisition and preparation software tools like SQL and Alteryx wor
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
k to get the data ready for analysis. Data visualization software tools work
Ws Ws Ws Ws Ws Ws Ws W s Ws Ws Ws Ws Ws
to both analyze data (as part of "Perform the Analysis" in the AMPS model)
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
and to communicate results (as part of "Share the Story" in the AMPS mod
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
el).
Brief Exercises
Ws
1. (LO1-1, LO1-2): Match the data analytics term to its data analytics definition:
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
© McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill L
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
LC.
1-3
mynursytest.store
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
Richardson
Richardson, Terrell, Teeter – Introduction to Data Analytics for Accounting, 2nd Edition – Chapter 1
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws W s Ws Ws Ws
Chapter 1 End-of-Chapter Assignment Solutions
Ws Ws Ws Ws
Multiple Choice Questions
Ws Ws
1. (LO 1-2) Which is the lowest level of critical thinking skills in Bloom’s Taxonomy?
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
a. Create
b. Remember
c. Apply
d. Analyze
2. (LO 1-2) Which is the highest level of critical thinking skills in Bloom’s Taxonomy?
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
a. Create
b. Apply
c. Analyze
d. Understand
3. (LO 1- Ws
2) Which is the appropriate ordering of critical thinking skills in Bloom’s Taxon
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
omy, where the “>” symbol means higher order skills?
Ws Ws Ws Ws Ws Ws Ws Ws
a. Remember > Apply Ws Ws
b. Apply > Analyze Ws Ws
c. Analyze > Evaluate Ws Ws
d. Create > Analyze Ws Ws
4. (LO 1-3) Which step of the AMPS model most appropriately addresses the axiom,
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
“Your data won’t speak unless you ask it the right data analytics questions”?
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
a. Ask the Question Ws Ws
b. Master the Data Ws Ws
c. Perform the Analysis Ws Ws
d. Share the Story Ws Ws
5. (LO 1- Ws
3) Which step of the AMPS model most appropriately addresses the question of the
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
best way to communicate data analytics findings with a decision maker?
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
a. Ask the Question Ws Ws
b. Master the Data Ws Ws
c. Perform the Analysis Ws Ws
d. Share the Story Ws Ws
6. (LO 1- Ws
3) What type of question is predicting whether a company will go bankrupt in the co
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ming two years? Ws Ws
a. What happened? What is happening? Ws Ws Ws Ws
b. Why did it happen? What are the root causes of past results? Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
c. Will it happen in the future? What is the probability something will
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
happen? Is it forecastable? Ws Ws Ws
© McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill L
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
LC.
1-1
mynursytest.store
,ACCESS Test Bank for Introduction to Data Analytics for Accounting 2nd Edition
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
Richardson
Richardson, Terrell, Teeter – Introduction to Data Analytics for Accounting, 2nd Edition – Chapter 1
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws W s Ws Ws Ws
d. What should we do based on what we expect will happen? How do we Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
optimize our performance based on potential constraints?” Ws Ws Ws Ws Ws Ws
7. (LO 1- Ws
3) What type of question is choosing to take certain tax deductions based on the w
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ay managers believe tax legislation will change in the near future?
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
a. What happened? What is happening? Ws Ws Ws Ws
b. Why did it happen? What are the root causes of past results?
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
c. Will it happen in the future? What is the probability something will happen? I
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
s it forecastable? Ws Ws
d. What should we do based on what we expect will happen? How do we Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
optimize our performance based on potential constraints?” Ws Ws Ws Ws Ws Ws
8. (LO 1- Ws
3) What type of question is finding the detail to more clearly understand why net in
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
come is decreasing when revenues are increasing?
Ws Ws Ws Ws Ws Ws
a. What happened? What is happening? Ws Ws Ws Ws
b. Why did it happen? What are the root causes of past results?Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
c. Will it happen in the future? What is the probability something will happen? I
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
s it forecastable? Ws Ws
d. What should we do based on what we expect will happen? How do we Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
optimize our performance based on potential constraints?” Ws Ws Ws Ws Ws Ws
9. (LO 1- Ws
4) What visualization type is most appropriate for evaluating the relationships betwe
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
en values?Ws
a. Bar chart Ws
b. Pie chart Ws
c. Histogram
d. Scatterplot
10. (LO 1-5) Which of the following software tools specialize in data visualizations?
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
a. SPSS and Power Query Ws Ws Ws
b. Alteryx and Tableau Prep Ws Ws Ws
c. Power BI and Tableau Ws Ws Ws
d. R and Python Ws Ws
Discussion Questions Ws
1. (LO 1- Ws
1) The computer is better at automated, repetitive tasks since it can be pro
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
grammed. The computer is also not subject to fatigue and can process ma W s Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ssive amounts of data easier than a human can. Most of the value-
Ws Ws Ws Ws Ws Ws Ws Ws W s Ws Ws Ws
added tasks and higher order thinking skills, such as analyzing, evaluating a
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
nd creating, are performed better by human accountants because they are
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws W
not easily programmed by a set of fixed rules. The ability
s Ws Ws Ws Ws Ws Ws Ws Ws W s Ws
© McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LL
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
C.
1-2
mynursytest.store
, ACCESS Test Bank for Introduction to Data Analytics for Accounting 2nd Edition
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
Richardson
Richardson, Terrell, Teeter – Introduction to Data Analytics for Accounting, 2nd Edition – Chapter 1
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws W s Ws Ws Ws
to recognize tradeoffs, evaluating alternatives, and evaluating ad hoc facts are
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
all better performed by humans.
Ws Ws Ws Ws Ws
2. (LO 1- Ws
2) The skills taught in the introduction to financial accounting were the lo
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
wer order thinking skills (noted in Bloom’s Taxonomy) such as rememberi
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ng, understanding and applying. Application of journal entries, computing
Ws Ws Ws W s Ws Ws Ws Ws Ws
trial balances, recording transactions, bank reconciliation, etc. are all exa
Ws Ws Ws Ws Ws Ws Ws Ws Ws
mples of lower order skills. Ws Ws Ws Ws
3. (LO 1- Ws
3) Accountants understand the tradeoffs between relevant data and reliabl
Ws Ws Ws Ws Ws Ws Ws Ws Ws
e data (such as that data which might exhibit more representational faithfu
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
lness).
Accountants also understand the tradeoffs between unstructured and struct Ws Ws Ws Ws Ws Ws Ws Ws
ured data, data internal or external to the company, and even the potential co
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
st of acquiring and processing the data as compared to the potential value pr
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ovided by use of the data. Ws Ws Ws Ws Ws
4. (LO 1- Ws
3) Mastering the data includes accessing, cleaning, and transforming the da
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ta to prepare the data for analysis.
Ws Ws Ws Ws Ws Ws
5. (LO 1- Ws
3) Data analytics might be viewed as successively peeling the layer of an oni
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
on. By peeling the first layer of the onion, you now are able to see the next la
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
yer and evaluate it and remove it to get to the third layer, etc. Often times,
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws W s Ws Ws
the AMPS model must be performed multiple times, refining the question (A
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
sk the Question), possibly considering different types of data (Master the Da
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ta), performing additional analysis (Perform the Analysis) and retelling the st
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
ory in each iteration (Sharing the Story) before the issue/problem/challenge
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
can be finally addressed with some confidence.
Ws Ws Ws Ws Ws Ws
6. (LO 1- Ws
3) Descriptive analysis reports what happened. Generally, evaluating the re
Ws Ws Ws Ws Ws W s Ws Ws Ws
venues and earnings performance starts with descriptive analysis and contin
Ws Ws Ws Ws Ws Ws Ws Ws Ws
ues with diagnostic analysis to understand “Why it happened?”.
Ws Ws Ws Ws Ws Ws Ws Ws
7. (LO 1-4, LO 1- Ws Ws Ws
5) Data acquisition and preparation software tools like SQL and Alteryx wor
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
k to get the data ready for analysis. Data visualization software tools work
Ws Ws Ws Ws Ws Ws Ws W s Ws Ws Ws Ws Ws
to both analyze data (as part of "Perform the Analysis" in the AMPS model)
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
and to communicate results (as part of "Share the Story" in the AMPS mod
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
el).
Brief Exercises
Ws
1. (LO1-1, LO1-2): Match the data analytics term to its data analytics definition:
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
© McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill L
Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws Ws
LC.
1-3
mynursytest.store
