Solution Manual for Introduction to Business Analytics, 1st Edition By Vernon Richardson and Marcia Watson Verified Chapter's 1 - 12 | Complete

Chapter 01 – Specify the Question: Using Business Analytics to Address Business Questions


Solution Manual for Introduction to Business Analytics,
1st Edition
By Vernon Richardson and Marcia Watson
Verified Chapter's 1 - 12 | Complete

, Chapter 01 – Specify the Question: Using Business Analytics to Address Business Questions




TABLE OF CONTENTS
Chapter 1: Specify the Question: Using Business Analytics to Address Business Questions

Chapter 2: Obtain the Data: An Introduction to Business Data Sources

Chapter 3: Analyze the Data: Basic Statistics and Tools Required in Business Analytics

Chapter 4: Analyze the Data: Exploratory Business Analytics (Descriptive Analytics and Diagnostic Analytics)

Chapter 5: Analyze the Data: Confirmatory Business Analytics (Predictive Analytics and Prescriptive Analytics)

Chapter 6: Report the Results: Using Data Visualization

Chapter 7: Marketing Analytics

Chapter 8: Accounting Analytics

Chapter 9: Financial Analytics

Chapter 10: Operations Analytics

Chapter 11: Advanced Business Analytics

Chapter 12: Using the SOAR Analytics Model to Put It All Together: Three Capstone Projects

, Chapter 01 – Specify the Question: Using Business Analytics to Address Business Questions



Chapter 1 End-of-Chapter Assignment Solutions
Multiple Choice Questions
1. (LO 1.1) A coordinated, standardized set of activities conducted by both people and equipment to accomplish a
specific business task is called .
a. business processes
b. business analysis
c. business procedure
d. business value

2. (LO 1.2) According to the information value chain, data combined with context is
a. Information.
b. Knowledge.
c. Insight.
d. Value.

3. (LO 1.5) Which phase of the SOAR analytics model addresses the proper way to communicate results to the
decision maker?
a. Specify the question
b. Obtain the data
c. Analyze the data
d. Report the results

4. (LO 1.5) Which phase of the SOAR analytics model involves finding the most appropriate data needed to address
the business question?
a. Specify the question
b. Obtain the data
c. Analyze the data
d. Report the results

5. (LO 1.5) Which questions seek information about Tesla’s sales in the next quarter?
a. What happened? What is happening?
b. Why did it happen? What are the causes of past results?
c. Will it happen in the future? What is the probability something will happen? Can we forecast what
will happen?
d. What should we do, based on what we expect will happen? How do we optimize our performance based
on potential constraints?


6. (LO 1.5) Which questions seek information on the routing of products from Queretaro, Mexico to Chicago,
United States in the last quarter?
a. What happened? What is happening?
b. Why did it happen? What are the causes of past results?
c. Will it happen in the future? What is the probability something will happen? Can we forecast what will
happen?
d. What should we do, based on what we expect will happen? How do we optimize our performance based
on potential constraints?

, Chapter 01 – Specify the Question: Using Business Analytics to Address Business Questions
7. (LO 1.5) Which squestions sask swhy snet sincome sis sincreasing swhen srevenues sare sdecreasing,
scounter stosexpectations?
a. What shappened? sWhat sis shappening?
b. Why sdid sit shappen? sWhat sare sthe scauses sof spast sresults?
c. Will sit shappen sin sthe sfuture? sWhat sis sthe sprobability ssomething swill shappen? sCan swe sforecast
swhat swillshappen?
d. What sshould swe sdo, sbased son swhat swe sexpect swill shappen? sHow sdo swe soptimize sour
sperformance sbasedson spotential sconstraints?

8. (LO s1.5) sWhich squestions shelp smanagers sunderstand show sto sorganize sfuture sshipments sbased son
sexpectedsdemand?
a. What shappened? sWhat sis shappening?
b. Why sdid sit shappen? sWhat sare sthe scauses sof spast sresults?
c. Will sit shappen sin sthe sfuture? sWhat sis sthe sprobability ssomething swill shappen? sCan swe sforecast
swhat swillshappen?
d. What sshould swe sdo, sbased son swhat swe sexpect swill shappen? sHow sdo swe soptimize sour
sperformancesbased son spotential sconstraints?

9. (LO s1.5) sWhich sterm srefers sto sthe scombined saccuracy, svalidity, sand sconsistency sof sdata sstored sand
sused soverstime?
a. Data sintegrity
b. Data soverload
c. Data svalue
d. Information svalue

10. (LO s1.3) sA sspecialist swho sknows show sto swork swith, smanipulate, sand sstatistically stest sdata sis sa
a. decision smaker.
b. data sscientist.
c. data sanalyst.
d. decision sscientist.

11. (LO s1.4) sWhich stype sof sanalysts spredicts sthe samount sof smoney sthat sa scompany swill sreceive sfrom sits
scustomers stoshelp smanagement sevaluate sfuture sinvestments sbased son sexpected sinvestment sperformance,
ssuch sas sinvestments sin sequipment sor semployee straining?
a. Marketing sanalyst
b. Operations sanalyst
c. Financial sanalyst
d. Accounting sanalyst

12. (LO s1.4) sWhich stype sof sanalyst saddresses squestions sregarding stax sand sauditing?
a. Marketing sanalyst
b. Operations sanalyst
c. Financial sanalyst
d. Accounting sanalyst

13. (LO s1.5) sSuppose sa scompany shas stimely sproduct sreviews sthat sare savailable swhen sneeded, sbut sthe
sreviews saresbiased. sThese sproduct sreviews sare swhich stype sof sdata?
a. Reliable
b. Relevant
c. Curated
d. Consistent
© sMcGraw sHill sLLC. sAll srights sreserved. sNo sreproduction sor sdistribution swithout sthe sprior swritten sconsent sof sMcGraw sHill
sLLC.


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