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 fquestions fask fwhy fnet fincome fis fincreasing fwhen frevenues fare fdecreasing,
fcounter ftofexpectations?
a. What fhappened? fWhat fis fhappening?
b. Why fdid fit fhappen? fWhat fare fthe fcauses fof fpast fresults?
c. Will fit fhappen fin fthe ffuture? fWhat fis fthe fprobability fsomething fwill fhappen? fCan fwe fforecast
fwhat fwillfhappen?
d. What fshould fwe fdo, fbased fon fwhat fwe fexpect fwill fhappen? fHow fdo fwe foptimize four fperformance
fbasedfon fpotential fconstraints?
8. (LO f1.5) fWhich fquestions fhelp fmanagers funderstand fhow fto forganize ffuture fshipments fbased fon
fexpectedfdemand?
a. What fhappened? fWhat fis fhappening?
b. Why fdid fit fhappen? fWhat fare fthe fcauses fof fpast fresults?
c. Will fit fhappen fin fthe ffuture? fWhat fis fthe fprobability fsomething fwill fhappen? fCan fwe fforecast
fwhat fwillfhappen?
d. What fshould fwe fdo, fbased fon fwhat fwe fexpect fwill fhappen? fHow fdo fwe foptimize four
fperformancefbased fon fpotential fconstraints?
9. (LO f1.5) fWhich fterm frefers fto fthe fcombined faccuracy, fvalidity, fand fconsistency fof fdata fstored fand
fused foverftime?
a. Data fintegrity
b. Data foverload
c. Data fvalue
d. Information fvalue
10. (LO f1.3) fA fspecialist fwho fknows fhow fto fwork fwith, fmanipulate, fand fstatistically ftest fdata fis fa
a. decision fmaker.
b. data fscientist.
c. data fanalyst.
d. decision fscientist.
11. (LO f1.4) fWhich ftype fof fanalysts fpredicts fthe famount fof fmoney fthat fa fcompany fwill freceive ffrom fits
fcustomers ftofhelp fmanagement fevaluate ffuture finvestments fbased fon fexpected finvestment fperformance,
fsuch fas finvestments fin fequipment for femployee ftraining?
a. Marketing fanalyst
b. Operations fanalyst
c. Financial fanalyst
d. Accounting fanalyst
12. (LO f1.4) fWhich ftype fof fanalyst faddresses fquestions fregarding ftax fand fauditing?
a. Marketing fanalyst
b. Operations fanalyst
c. Financial fanalyst
d. Accounting fanalyst
13. (LO f1.5) fSuppose fa fcompany fhas ftimely fproduct freviews fthat fare favailable fwhen fneeded, fbut fthe
freviews farefbiased. fThese fproduct freviews fare fwhich ftype fof fdata?
a. Reliable
b. Relevant
c. Curated
d. Consistent
© fMcGraw fHill fLLC. fAll frights freserved. fNo freproduction for fdistribution fwithout fthe fprior fwritten fconsent fof fMcGraw fHill fLLC.
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