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 p1.5) pWhich pquestions pask pwhy pnet pincome pis pincreasing pwhen prevenues pare pdecreasing,
pcounter ptopexpectations?
a. What phappened? pWhat pis phappening?
b. Why pdid pit phappen? pWhat pare pthe pcauses pof ppast presults?
c. Will pit phappen pin pthe pfuture? pWhat pis pthe pprobability psomething pwill phappen? pCan pwe
pforecast pwhat pwillphappen?
d. What pshould pwe pdo, pbased pon pwhat pwe pexpect pwill phappen? pHow pdo pwe poptimize pour
pperformance pbasedpon ppotential pconstraints?
8. (LO p1.5) pWhich pquestions phelp pmanagers punderstand phow pto porganize pfuture pshipments pbased
pon pexpectedpdemand?
a. What phappened? pWhat pis phappening?
b. Why pdid pit phappen? pWhat pare pthe pcauses pof ppast presults?
c. Will pit phappen pin pthe pfuture? pWhat pis pthe pprobability psomething pwill phappen? pCan pwe
pforecast pwhat pwillphappen?
d. What pshould pwe pdo, pbased pon pwhat pwe pexpect pwill phappen? pHow pdo pwe poptimize pour
pperformancepbased pon ppotential pconstraints?
9. (LO p1.5) pWhich pterm prefers pto pthe pcombined paccuracy, pvalidity, pand pconsistency pof pdata pstored
pand pused poverptime?
a. Data pintegrity
b. Data poverload
c. Data pvalue
d. Information pvalue
10. (LO p1.3) pA pspecialist pwho pknows phow pto pwork pwith, pmanipulate, pand pstatistically ptest pdata pis pa
a. decision pmaker.
b. data pscientist.
c. data panalyst.
d. decision pscientist.
11. (LO p1.4) pWhich ptype pof panalysts ppredicts pthe pamount pof pmoney pthat pa pcompany pwill preceive pfrom pits
pcustomers ptophelp pmanagement pevaluate pfuture pinvestments pbased pon pexpected pinvestment
pperformance, psuch pas pinvestments pin pequipment por pemployee ptraining?
a. Marketing panalyst
b. Operations panalyst
c. Financial panalyst
d. Accounting panalyst
12. (LO p1.4) pWhich ptype pof panalyst paddresses pquestions pregarding ptax pand pauditing?
a. Marketing panalyst
b. Operations panalyst
c. Financial panalyst
d. Accounting panalyst
13. (LO p1.5) pSuppose pa pcompany phas ptimely pproduct previews pthat pare pavailable pwhen pneeded, pbut pthe
previews parepbiased. pThese pproduct previews pare pwhich ptype pof pdata?
a. Reliable
b. Relevant
c. Curated
d. Consistent
© pMcGraw pHill pLLC. pAll prights preserved. pNo preproduction por pdistribution pwithout pthe pprior pwritten pconsent pof pMcGraw pHill
pLLC.
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