Applied Statistics in Business and Economics
@xc @xc @xc @xc @xc
7th Edition by David Doane
@xc @xc @xc @xc
, ASBE @xc7e @xcSolutions @xcfor @xcInstructors
1.1 Chapter @xc1 a. Statistics can be used to 1) determine what a typical commission is and then
2) use that value to identify commissions that appear to be unusually high.
b. She could use statistics to show the average energy use compared to previous models.
She could also use statistics to show how durable the monitor would be in the field.
c. He could use statistics to calculate the average absenteeism at each plant and then
compare across the three plants.
d. He could calculate average number of defects in each shipment. He could determine
variation in number of defects between the three shipments.
Learning Objective: 01-1
1.2 a. He could calculate the job turnover for each gender for each restaurant. He could then
look at the difference between the various restaurants as well as the difference between
genders.
b. He could calculate the average number of emails received and sent for employees in
different job classifications and make comparisons.
c. The portfolio manager could calculate both the average return and the variation on return
for the six different investments and make comparisons.
d. By studying the busiest times of day for surgery, the administrator could work with
surgeons to spread their surgeries out to better use the facilities. He might also look at
which surgeries take the longest and which are shorter to help with scheduling.
Learning Objective: 01-1
1.3 a. The average business school graduate should expect to use computers to manipulate the
data.
b. Answers will vary. Weak quantitative skills lead to poor decision making because data-
based decision making is a hallmark of successful businesses. If one cannot analyze
data or understand summary analyses, one will be making decisions without full
information.
Learning Objective: 01-2
1.4 a. Answers will vary. Why Not Study: It is difficult to become a statistical “expert” after
taking one introductory college course. A business person should hire statistical experts
and have faith that those who are using statistics are doing it correctly. Why Study: In
fact, most college graduates will use statistics every day. Relying on a consultant to
perform simple or even complex statistical analyses means turning over part of the
business decision-making to someone who doesn’t know your business as well as you
do.
b. Answers will vary. Answers provided in part a will be similar for the subjects of
accounting. Foreign languages are essential in this global business environment of
today. While learning a foreign language can take considerably more time as an adult,
the investment is worth it. Businesses are looking for college graduates that have
@
1
, quantitative @xcskills @xcand @xcspeak @xca @xcforeign @xclanguage. @xcChinese @xcand @xcSpanish @xcare
@xcpopular @xcchoices.
c. @xcTo @xcarrive @xcat @xcan @xcabsurd @xcresult, @xcand @xcthen @xcconclude @xcthe @xcoriginal
@xcassumption @xc must @xc have @xc been @xc wrong, @xc since @xcit @xc gave @xcus @xcthis @xcabsurd
@xcresult. @xcThis @xcis @xcalso @xcknown @xcas @xcproof @xc by @xc contradiction. @xcIt @xc makes @xcuse
@xc of @xcthe @xclaw @xcof @xcexcluded @xc middle @xc— @xc a @xc statement @xc which @xc cannot @xc be
@xcfalse, @xc must @xcthen @xc be @xctrue. @ x c If @xc you @xc state @xcthat @xc you @xc will @xc never @xc use
@xc statistics @xcin @xc your @xc business @xc profession @xcthen @xc you @xc might @xcconclude @xcthat
@xc you @xc shouldn’t @xc study @xc statistics. @xc However, @xcthe @xc original @xc assumption @xcof
@xcnever @xcusing @xcstatistics @xcis @xcwrong; @xctherefore @xcthe @xcconclusion @xcof @xcnot @xcneeding
@xcto @xc study @xc statistics @xcis @xcalso @xc wrong.
Learning @ x c Objective: @ x c 01-2
1.5 a. @xcAnswers @xcwill @xcvary.
b. @xcAn @xchour @xcwith @xcan @xcexpert @xcat @xcthe @xcbeginning @xcof @xca @xcproject @xccould @xcbe @xcthe
@xc smartest @xc move @xc a @xc manager @xc can @xc make. @xc A @xc consultant @xcis @xchelpful @xc when
@xcyour @xcteam @xclacks @xccertain @xccritical @xc skills, @xc or @xc when @xcan @xcunbiased @xc or
@xcinformed @xc view @xc cannot @xc be @xc found @xcinside @xc your @xc organization. @xc Expert
@xcconsultants @xccan @xc handle @xc domineering @xc or @xcindecisive @xcteam @xc members,
@xc personality @xc clashes, @xcfears @xcabout @xcadverse @xcfindings, @xcand @xclocal @xcpolitics. @xcAs
@xcin @xcany @xcbusiness @xcdecision, @xcthe @xc costs @xc of @xc paying @xc for @xc statistical @xc assistance
@xc must @xc be @xc weighed @xcagainst @xcthe @xc benefits. @ x c Costs @xcare: @xc statistician’s @xctime,
@xc more @xctime @xcinvested @xcin @xcthe @xc beginning @xc of @xc a @xc project @xc which @xc may @xc mean
@xcresults @xcare @xc not @xcimmediate. @xcBenefits @xcinclude: @xc better @xc sampling @xc strategies
@xc which @xccan @xcresult @xcin @xc more @xcuseful @xc data, @xca @xc better @xc understanding @xc of
@xcwhat @xcinformation @xc can @xc be @xc extracted @xc from @xcthe @xc data, @xc greater @xc confidence
@xcin @xcthe @xcresults.
Learning @ x c Objective: @ x c 01-2
1.6 a. @ x c The @xcethical @xcissue @xcis @xcthat @xccredit @xccard @xccompanies @xcare @xcusing @xcunfair
@xc marketing @xc practices @xcto @xc entice @xc students @xcto @xc use @xc credit @xccards. @xc Students
@xcare @xc a @xc vulnerable @xc group @xcthat @xc has @xc not @xc been @xc educated @xc about @xc personal
@xcfinance. @xc Credit @xc card @xccompanies @xcare @xcalso @xc purchasing @xc student @xc lists
@xcfrom @xcuniversities @xcand @xcvarious @xcstudent @xcgroups. @xcThis @xcis @xcalso @xcan @xcunethical
@xcpractice @xcby @xcthe @xc universities.
b. They @xcused @xcan @xcin-person @xcsurvey @xcgiven @xcto @xc1500 @xcstudents. @ x c These
@xc students @xc were @xcrandomly @xc solicited @xcat @xc popular @xc places @xc on @xc campus. @xc The
@xc sampling @xctechnique @xc was @xca @xcconvenience @xc sample. @xcThe @xcreport @xc did @xc not
@xcattempt @xcto @xc make @xcinferences @xcabout @xcthe @xc population @xc of @xc college @xc students.
@xcThe @xcreport @xc simply @xc provided @xc statistics @xccollected @xcfrom @xctheir @xc sample.
@xcThe @xc naïve @xcreader @xcwould @xcmost @xclikely @xcmake @xcthe @xcinference @xcthat @xcthe
@xcnumbers @xcfrom @xcthe @xcsample @xcapply @xcto @xcthe @xc population @xcas @xca @xc whole. @xc This
@xc should @xc be @xc made @xc clearer @xcin @xcthe @xcreport.
c. The @xcsubjects @xcsurveyed @xcinclude @xc1) @xchow @xcstudents @xcpay @xcfor @xctheir @xceducation,
@xc2) @xchow @xcthey @xcuse @xccredit @xccards, @xc3) @xc how @xcmany @xcof @xcthem @xcuse @xccredit
@xccards, @xcand @xc4) @xcattitudes @xctoward @xccredit @xccard @xc marketing @xcon @xccampus. @xcIt
@xcwould @xcbe @xcinteresting @xcto @xc see @xc what @xcthe @xcquestions @xcwere @xcand @xchow @xcthey
@xc were @xc worded.
2
, d. Because @xcthe @xcsurvey @xcfocused @xcon @xcstudents’ @xcopinions @xcand @xcdid @xcnot @xcprovide
@xcinformation @xcabout @xccredit @xc card @xcuse @xc by @xcthe @xc general @xc population @xcit @xcis
@xc difficult @xcto @xc conclude @xcthat @xcthe @xc marketing @xc practices @xccompanies @xc use @xc on
@xccampuses @xcare @xc different @xc from @xcthose @xc used @xcto @xc market @xc cards @xcto @xcthe
@xc general @xc public. @xc Furthermore, @xcit @xc was @xcnot @xc obvious @xcthat @xcthose @xc students
@xc who @xc had @xc credit @xc cards @xcobtained @xcthose @xccards @xcas @xca @xc direct @xcresult @xcof @xcthe
@xccampus @xcmarketing @xcefforts. @xcWhat @xcwas @xcimpressive @xc was @xcthe @xcamount @xc of
@xcresearch @xcthe @xc group @xc did @xcto @xc support @xctheir @xcclaims @xc of @xc unethical @xc practices.
@xcThe @xc references @xcto @xc other @xc reports @xcand @xc court @xc cases @xc were @xca @xc better
@xc support @xcfor @xctheir @xcclaims @xcin @xc question @xc 1.
3
@xc @xc @xc @xc @xc
7th Edition by David Doane
@xc @xc @xc @xc
, ASBE @xc7e @xcSolutions @xcfor @xcInstructors
1.1 Chapter @xc1 a. Statistics can be used to 1) determine what a typical commission is and then
2) use that value to identify commissions that appear to be unusually high.
b. She could use statistics to show the average energy use compared to previous models.
She could also use statistics to show how durable the monitor would be in the field.
c. He could use statistics to calculate the average absenteeism at each plant and then
compare across the three plants.
d. He could calculate average number of defects in each shipment. He could determine
variation in number of defects between the three shipments.
Learning Objective: 01-1
1.2 a. He could calculate the job turnover for each gender for each restaurant. He could then
look at the difference between the various restaurants as well as the difference between
genders.
b. He could calculate the average number of emails received and sent for employees in
different job classifications and make comparisons.
c. The portfolio manager could calculate both the average return and the variation on return
for the six different investments and make comparisons.
d. By studying the busiest times of day for surgery, the administrator could work with
surgeons to spread their surgeries out to better use the facilities. He might also look at
which surgeries take the longest and which are shorter to help with scheduling.
Learning Objective: 01-1
1.3 a. The average business school graduate should expect to use computers to manipulate the
data.
b. Answers will vary. Weak quantitative skills lead to poor decision making because data-
based decision making is a hallmark of successful businesses. If one cannot analyze
data or understand summary analyses, one will be making decisions without full
information.
Learning Objective: 01-2
1.4 a. Answers will vary. Why Not Study: It is difficult to become a statistical “expert” after
taking one introductory college course. A business person should hire statistical experts
and have faith that those who are using statistics are doing it correctly. Why Study: In
fact, most college graduates will use statistics every day. Relying on a consultant to
perform simple or even complex statistical analyses means turning over part of the
business decision-making to someone who doesn’t know your business as well as you
do.
b. Answers will vary. Answers provided in part a will be similar for the subjects of
accounting. Foreign languages are essential in this global business environment of
today. While learning a foreign language can take considerably more time as an adult,
the investment is worth it. Businesses are looking for college graduates that have
@
1
, quantitative @xcskills @xcand @xcspeak @xca @xcforeign @xclanguage. @xcChinese @xcand @xcSpanish @xcare
@xcpopular @xcchoices.
c. @xcTo @xcarrive @xcat @xcan @xcabsurd @xcresult, @xcand @xcthen @xcconclude @xcthe @xcoriginal
@xcassumption @xc must @xc have @xc been @xc wrong, @xc since @xcit @xc gave @xcus @xcthis @xcabsurd
@xcresult. @xcThis @xcis @xcalso @xcknown @xcas @xcproof @xc by @xc contradiction. @xcIt @xc makes @xcuse
@xc of @xcthe @xclaw @xcof @xcexcluded @xc middle @xc— @xc a @xc statement @xc which @xc cannot @xc be
@xcfalse, @xc must @xcthen @xc be @xctrue. @ x c If @xc you @xc state @xcthat @xc you @xc will @xc never @xc use
@xc statistics @xcin @xc your @xc business @xc profession @xcthen @xc you @xc might @xcconclude @xcthat
@xc you @xc shouldn’t @xc study @xc statistics. @xc However, @xcthe @xc original @xc assumption @xcof
@xcnever @xcusing @xcstatistics @xcis @xcwrong; @xctherefore @xcthe @xcconclusion @xcof @xcnot @xcneeding
@xcto @xc study @xc statistics @xcis @xcalso @xc wrong.
Learning @ x c Objective: @ x c 01-2
1.5 a. @xcAnswers @xcwill @xcvary.
b. @xcAn @xchour @xcwith @xcan @xcexpert @xcat @xcthe @xcbeginning @xcof @xca @xcproject @xccould @xcbe @xcthe
@xc smartest @xc move @xc a @xc manager @xc can @xc make. @xc A @xc consultant @xcis @xchelpful @xc when
@xcyour @xcteam @xclacks @xccertain @xccritical @xc skills, @xc or @xc when @xcan @xcunbiased @xc or
@xcinformed @xc view @xc cannot @xc be @xc found @xcinside @xc your @xc organization. @xc Expert
@xcconsultants @xccan @xc handle @xc domineering @xc or @xcindecisive @xcteam @xc members,
@xc personality @xc clashes, @xcfears @xcabout @xcadverse @xcfindings, @xcand @xclocal @xcpolitics. @xcAs
@xcin @xcany @xcbusiness @xcdecision, @xcthe @xc costs @xc of @xc paying @xc for @xc statistical @xc assistance
@xc must @xc be @xc weighed @xcagainst @xcthe @xc benefits. @ x c Costs @xcare: @xc statistician’s @xctime,
@xc more @xctime @xcinvested @xcin @xcthe @xc beginning @xc of @xc a @xc project @xc which @xc may @xc mean
@xcresults @xcare @xc not @xcimmediate. @xcBenefits @xcinclude: @xc better @xc sampling @xc strategies
@xc which @xccan @xcresult @xcin @xc more @xcuseful @xc data, @xca @xc better @xc understanding @xc of
@xcwhat @xcinformation @xc can @xc be @xc extracted @xc from @xcthe @xc data, @xc greater @xc confidence
@xcin @xcthe @xcresults.
Learning @ x c Objective: @ x c 01-2
1.6 a. @ x c The @xcethical @xcissue @xcis @xcthat @xccredit @xccard @xccompanies @xcare @xcusing @xcunfair
@xc marketing @xc practices @xcto @xc entice @xc students @xcto @xc use @xc credit @xccards. @xc Students
@xcare @xc a @xc vulnerable @xc group @xcthat @xc has @xc not @xc been @xc educated @xc about @xc personal
@xcfinance. @xc Credit @xc card @xccompanies @xcare @xcalso @xc purchasing @xc student @xc lists
@xcfrom @xcuniversities @xcand @xcvarious @xcstudent @xcgroups. @xcThis @xcis @xcalso @xcan @xcunethical
@xcpractice @xcby @xcthe @xc universities.
b. They @xcused @xcan @xcin-person @xcsurvey @xcgiven @xcto @xc1500 @xcstudents. @ x c These
@xc students @xc were @xcrandomly @xc solicited @xcat @xc popular @xc places @xc on @xc campus. @xc The
@xc sampling @xctechnique @xc was @xca @xcconvenience @xc sample. @xcThe @xcreport @xc did @xc not
@xcattempt @xcto @xc make @xcinferences @xcabout @xcthe @xc population @xc of @xc college @xc students.
@xcThe @xcreport @xc simply @xc provided @xc statistics @xccollected @xcfrom @xctheir @xc sample.
@xcThe @xc naïve @xcreader @xcwould @xcmost @xclikely @xcmake @xcthe @xcinference @xcthat @xcthe
@xcnumbers @xcfrom @xcthe @xcsample @xcapply @xcto @xcthe @xc population @xcas @xca @xc whole. @xc This
@xc should @xc be @xc made @xc clearer @xcin @xcthe @xcreport.
c. The @xcsubjects @xcsurveyed @xcinclude @xc1) @xchow @xcstudents @xcpay @xcfor @xctheir @xceducation,
@xc2) @xchow @xcthey @xcuse @xccredit @xccards, @xc3) @xc how @xcmany @xcof @xcthem @xcuse @xccredit
@xccards, @xcand @xc4) @xcattitudes @xctoward @xccredit @xccard @xc marketing @xcon @xccampus. @xcIt
@xcwould @xcbe @xcinteresting @xcto @xc see @xc what @xcthe @xcquestions @xcwere @xcand @xchow @xcthey
@xc were @xc worded.
2
, d. Because @xcthe @xcsurvey @xcfocused @xcon @xcstudents’ @xcopinions @xcand @xcdid @xcnot @xcprovide
@xcinformation @xcabout @xccredit @xc card @xcuse @xc by @xcthe @xc general @xc population @xcit @xcis
@xc difficult @xcto @xc conclude @xcthat @xcthe @xc marketing @xc practices @xccompanies @xc use @xc on
@xccampuses @xcare @xc different @xc from @xcthose @xc used @xcto @xc market @xc cards @xcto @xcthe
@xc general @xc public. @xc Furthermore, @xcit @xc was @xcnot @xc obvious @xcthat @xcthose @xc students
@xc who @xc had @xc credit @xc cards @xcobtained @xcthose @xccards @xcas @xca @xc direct @xcresult @xcof @xcthe
@xccampus @xcmarketing @xcefforts. @xcWhat @xcwas @xcimpressive @xc was @xcthe @xcamount @xc of
@xcresearch @xcthe @xc group @xc did @xcto @xc support @xctheir @xcclaims @xc of @xc unethical @xc practices.
@xcThe @xc references @xcto @xc other @xc reports @xcand @xc court @xc cases @xc were @xca @xc better
@xc support @xcfor @xctheir @xcclaims @xcin @xc question @xc 1.
3
