Complete the following assignments using excel and the following template: · Assignment – Statement · Identify Business Problem - “Define Problem statement [aka Case Analysis Assignment]” · Analytics Tools and Models used and results · Interpretation, Discussion, and Analysis of Findings and Results – Interpretation, Discussion, and Analysis of outcomes and results of Analytics Tools and Models used · Tip: Support your Interpretation, Discussion, and Analysis of Results with the numbers you developed in your: · Analytics Tools and Models used results · Business Analytics Case Analysis EXCEL model(s) and outcomes and results · Recommendations Assignment information: The worksheet Purchasing Survey in the Performance Lawn Care database provides data related to predicting the level of business (Usage Level) obtained from a third-party survey of purchasing managers of customers Performance Lawn Care. The seven PLE attributes rated by each respondent are 8 The data and description of this case are based on the HATCO example on pages 28–29 in Joseph F. Hair, Jr., Rolph E. Anderson, Ronald L. Tatham, and William C. Black, Multivariate Analysis, 5th ed. (Upper Saddle River, NJ: Prentice Hall, 1998). ·                   Delivery speed —the amount of time it takes to deliver the product once an order is confirmed ·                   Price level —the perceived level of price charged by PLE ·                   Price flexibility —the perceived willingness of PLE representatives to negotiate price on all types of purchases ·                   Manufacturing image —the overall image of the manufacturer ·                   Overall service —the overall level of service necessary for maintaining a satisfactory relationship between PLE and the purchaser ·                   Sales force image —the overall image of the PLE’s sales force ·                   Product quality —perceived level of quality Responses to these seven variables were obtained using a graphic rating scale, where a 10-centimeter line was drawn between endpoints labeled “poor” and “excellent.” Respondents indicated their perceptions using a mark on the line, which was measured from the left endpoint. The result was a scale from 0 to 10 rounded to one decimal place. Two measures were obtained that reflected the outcomes of the respondent’s purchase relationships with PLE: ·                   Usage level —how much of the firm’s total product is purchased from PLE, measured on a 100-point scale, ranging from 0% to 100% ·                   Satisfaction level —how satisfied the purchaser is with past purchases from PLE, measured on the same graphic rating scale as perceptions 1 through 7 The data also include four characteristics of the responding firms: ·                   Size of firm —size relative to others in this market (0=small;1=large)(0=small;1=large) ·                   Purchasing structure —the purchasing method used in a particular company (1=centralized procurement,0=dec ...

1. Complete the following assignments using excel and the
following template:
· Assignment – Statement
· Identify Business Problem -
“Define Problem statement [aka Case Analysis Assignment]”
· Analytics Tools and Models used and results
· Interpretation, Discussion, and Analysis of Findings and
Results –
Interpretation, Discussion, and Analysis of outcomes and results
of Analytics Tools and Models used
· Tip: Support your Interpretation, Discussion, and Analysis of
Results with the numbers you developed in your:
· Analytics Tools and Models used results
· Business Analytics Case Analysis EXCEL model(s) and
outcomes and results
· Recommendations
Assignment information:
The worksheet Purchasing Survey in the Performance Lawn
Care database provides data related to predicting the level of
business (Usage Level) obtained from a third-party survey of
purchasing managers of customers Performance Lawn Care.
The seven PLE attributes rated by each respondent are
8 The data and description of this case are based on the HATCO
example on pages 28–29 in Joseph F. Hair, Jr., Rolph E.

2. Anderson, Ronald L. Tatham, and William C.
Black, Multivariate Analysis, 5th ed. (Upper Saddle River, NJ:
Prentice Hall, 1998).
· Delivery speed —the amount of time it takes to
deliver the product once an order is confirmed
· Price level —the perceived level of price charged
by PLE
· Price flexibility —the perceived willingness of
PLE representatives to negotiate price on all types of purchases
· Manufacturing image —the overall image of the
manufacturer
· Overall service —the overall level of service
necessary for maintaining a satisfactory relationship between
PLE and the purchaser
· Sales force image —the overall image of the
PLE’s sales force
· Product quality —perceived level of quality
Responses to these seven variables were obtained using a
graphic rating scale, where a 10-centimeter line was drawn
between endpoints labeled “poor” and “excellent.” Respondents
indicated their perceptions using a mark on the line, which was
measured from the left endpoint. The result was a scale from 0
to 10 rounded to one decimal place.
Two measures were obtained that reflected the outcomes of the
respondent’s purchase relationships with PLE:
· Usage level —how much of the firm’s total
product is purchased from PLE, measured on a 100-point scale,
ranging from 0% to 100%
· Satisfaction level —how satisfied the purchaser is
with past purchases from PLE, measured on the same graphic
rating scale as perceptions 1 through 7
The data also include four characteristics of the responding

3. firms:
· Size of firm —size relative to others in this
market (0=small;1=large)(0=small;1=large)
· Purchasing structure —the purchasing method
used in a particular company (1=centralized
procurement,0=decentralized procurement)(1=centralized
procurement,0=decentralized procurement)
· Industry —the industry classification of the
purchaser [1=retail(resale such as Home
Depot),0=private(nonresale, such as a
landscaper)][1=retail(resale such as Home
Depot),0=private(nonresale, such as a landscaper)]
· Buying type —a variable that has three
categories (1=new purchase,2=modified
rebuy,3=straight rebuy)(1=new purchase,2=modified
rebuy,3=straight rebuy)
Assignment 1:
· Elizabeth Burke would like to understand what she learned
from these data.
· Apply appropriate data-mining techniques to analyze the data.
For example, can PLE segment customers into groups with
similar perceptions about the company?
· Can cause-and-effect models provide insight about the drivers
of satisfaction and usage level? Summarize your results in a
report to Ms. Burke.
Running Head: RESEARCH DESIGN IN SOCIAL WORK
1
RESEARCH DESIGN IN SOCIAL WORK

4. 2
Research Design on Impacts of Tobacco Law
Name:
Institution:
Research Design on Impacts of Tobacco Law
Methodology
Brief introduction
The methodology section will mainly help in the answering
the questions on how to collect data and its analysis. It is
evident that the paper will mainly depend on the qualitative
research method as it is a social issue and it will mainly help
with the interactions of the youths in Florida in regards to the
tobacco law. The introduction of the process is relevant to the
entire research process because it will help with the collection
of data and interacting with the public.
Participants
The research will target the general interactions of the society
towards the tobacco law and how it is affecting the youths. It is

5. evident that the research will mainly include the 100 youths
aged between the ages of 18-35 and also 100 adults of the ages
above 35 years. The gender divisions in the research will be
evenly distributed to help with the balance of the interactions
across the research which is vital for increasing the divisions of
data. It is also evident that the participants will be from various
parts of the county and from different social divisions in the
society which is relevant to the way that they will provide a
diverse response and opinions regarding the topic of interaction.
Description of the instrument(s)
The research was focusing on the main use of the
instruments such as the surveys, questionnaires and the internet
as the main sources of information. It is important to understand
that the questionnaires will both be applicable on the internet
and in the physical world. The current restrictions of the Covid-
19 pandemic will make it impossible for physical interactions
but they will mainly be interactive via the various social media
platforms and even the phone through text and making calls.
The questionnaires will comprise of consistent questions
formulated to help answer the relevant questions to provide the
insights into research questions.
The utilization of the surveys as a research instrument is
vital in the production and efficiency because it is important to
make the right strategy that will see more information and
insight into the research. It is evident that the research will
benefit from surveys because they will help with the extensive
amount of information and how to make sure that they interact
on a basis of such caliber. The surveys incorporate other
methods such as questionnaires and the interviews to help bring
in large amounts of data to make sure that they have to increase
their participations.
The application of the internet as an instrument mainly
involves seeking information, people’s comments and opinions
from various social media platforms and websites and thus help
compile it for the research. It is relevant to understand that the
method is relevant because there are numerous researches into

6. the research topic in the society that will help make it easier to
interact and increase interactions into the research area. There
is high dependence of the society on the internet that makes it
important to establish a pattern that will help in taking
advantage and increase the efficiency of the method.
Steps/procedures/process to get your research completed
including the way that you review your literature
The method will mainly comprise of basic principles such
as identifying and developing a topic of interaction which is
vital for the development of a flow of the research. It is
important to conduct the background information into the
research topic to help get the insight whereby the involved
parties, the potential outcomes and how to establish a solution
or positioning of the research. The use of catalogs to help with
the finding of the right media and books is an essential stage
that is vital for their performance and interactions that is
essential for practicality. The collection and sourcing of the
data from the books, the media, internet resources and journal
articles will help in the conducting of the literature review
which is more practical and efficient in formulating the right
hypothesis of the research. The literature review will mainly
incorporate the descriptive analysis that will make sure that
there is proper integration of the data but from a social and
interactive platform.
Data Analysis/how you are going to analyze data
Data analysis is the most important part of the research
because it compiles of breaking down information and making
sure that it is relevant and practical in relation to the research
question. The data analysis will take advantage of the content
analysis and cluster analysis. The content analysis is relevant
because it focuses on establishing a proper insight into the
collected information and it focuses on the different types of
information and groupings in the research team. It is important
to make sure that there is a major advantage to integrate the
content analysis because it brings out the best interpretation of
data and making sure that it is productive. The cluster analysis

7. helps in the breaking down data and creating sufficient grouping
and because the research is revolving on a wider research area it
will be relevant to apply the method as a way of helping their
interactions.
References
Berman, M. (2016). Raising the Tobacco Sales Age To 21:
Surveying the Legal Landscape. Public Health Reports
(1974-),131(2), 378-381.
Reddy, K., Yadav, A., Arora, M., & Nazar, G. (2012).
Integrating Tobacco Control into Health and Development
Agendas. Tobacco Control,21(2), 281-286.
Akers, R., Krohn, M., Lanza-Kaduce, L., & Radosevich, M.
(2017). Social Learning and Deviant Behavior: A Specific
Test of a General Theory. American Sociological
Review,44(4), 636-655. doi:10.2307/2094592
Cismaru, M., & Lavack, A. (2017). Tobacco Warning Labels
and the Protection Motivation Model: Implications for Florida
Tobacco Control Policy. Florida Public Policy / Analyse De
Politiques,33(4), 477-486.
Dealer SatisfactionDealer Satisfaction Survey
Scale:012345Sample North
AmericaSize201010214221150201100214201450201211183415
6020131261234451002014235154456125South
America2010000262102011000262102012001411143020130113
12335020141124226090Europe201000137415201100128415201
200121572520130012216302014001417830Pacific
Rim20100012205201100113052012001 13162013000253102014
00127212China2012000100120130014207201400158216

8. End-User SatisfactionEnd-User SatisfactionSample North
America012345Size201013615373810020111241835401002012
12517344110020130241533461002014023153149100South
America2010125183638100201113617363710020120261937361
0020130252037361002014025193737100Europe2010124213636
10020111252134371002012114263731100201311317413710020
14012194533100Pacific
Rim2010235154134100201112715413410020121251640361002
0130241740371002014013194235100China20120336281050201
3122430115020140113311450
2014 Customer Survey2014 Customer SurveyRegionQualityEase
of
UsePriceServiceNA4134NA4445NA4543NA5444NA5454NA55
35NA5442NA5545NA4445NA4545NA4514NA5544NA5433NA
4544NA5435NA5525NA5425NA5425NA4544NA4454NA4424N
A4334NA5525NA5343NA5445NA5525NA5553NA4454NA5444
NA5155NA5435NA4514NA4435NA5344NA5524NA5444NA55
44NA5545NA4335NA5443NA5434NA5515NA5454NA3434NA
5424NA5545NA5534NA5444NA5444NA5445NA5414NA5455N
A5534NA5445NA4355NA5444NA5555NA5545NA4444NA5455
NA4554NA5554NA5535NA5444NA5452NA4455NA4445NA54
44NA5435NA5454NA5545NA5444NA5452NA5345NA5455NA
5415NA4535NA3525NA5544NA4435NA3245NA1434NA4535N
A5544NA4555NA5545NA5544NA4245NA5454NA5454NA5543
NA5555NA4553NA5545NA4455NA5534NA4524NA5554NA45
43NA4554SA5435SA5424SA5455SA4245SA5445SA4525SA54
44SA4535SA4443SA4424SA5434SA3355SA5434SA5425SA443
4SA4435SA1534SA5424SA4444SA4455SA5424SA4455SA4443
SA3345SA5444SA4441SA4555SA4145SA4544SA4445SA5434S
A4445SA5543SA5544SA4424SA4445SA5445SA5444SA5414S
A3445SA4354SA4423SA5433SA4345SA5355SA5444SA5444S
A3434SA4414SA4343Eur4553Eur4442Eur3454Eur3413Eur4455
Eur5555Eur5551Eur4554Eur3444Eur3533Eur4454Eur5455Eur5
344Eur5545Eur3444Eur4545Eur4544Eur5445Eur4544Eur3534E
ur4442Eur5534Eur5345Eur4524Eur4344Eur5433Eur2444Eur545
4Eur4543Eur5415Pac5445Pac5555Pac4444Pac4344Pac5454Pac

9. 4444Pac5545Pac4233Pac3444Pac5445China5544China5543Chi
na4433China4433China4432China4433China4432China3433Chi
na3422China2321
Complaints ComplaintsMonthWorldNASAEurPacChinaJan-
1016910212523Feb-1018711513554Mar-1021012815616Apr-
1022613616677May-1023213717735Jun-1026115119829Jul-
1024514018807Aug-1022312816763Sep-1019510315734Oct-
101749614622Nov-101548411590Dec-10163999541Jan-
1119512310593Feb-1122114113625Mar-1124015216666Apr-
11264163207011May-1128317822758Jun-11296170288612Jul-
11269153258110Aug-1125614623798Sep-1123113120737Oct-
1121412516685Nov-1120111813664Dec-111719611613Jan-
12200112156643Feb-12216117187164Mar-
12234126207693Apr-122531382379112May-
122821522685145Jun-123051633091156Jul-
122961562889185Aug-122791482686154Sep-
122661432482134Oct-122431312176123Nov-
122321281873103Dec-12203107157074Jan-
13216110197485Feb-132391232379104Mar-
132661382683136Apr-132841503088115May-
133151693391157Jun-133401813795198Jul-
133191693492177Aug-133041603290157Sep-
132771412987146Oct-132501232683126Nov-
132281122477105Dec-13213105237474Jan-
14240121268085Feb-142511262882105Mar-
142811483185125Apr-142981553589136May-
143221683995128Jun-1435018343981511Jul-
1433017041951410Aug-143111583893139Sep-
142891493389117Oct-14265136308586Nov-
14239121268075Dec-14219108237675
Mower Unit SalesMower Unit
SalesMonthNASAEuropePacificChinaWorldJan-
10600020072010007020Feb-10795022099012009280Mar-
108100250132011009780Apr-1090502801650120011100May-
1099003101590130011930Jun-10102003001620120012240Jul-
1087302801590140010740Aug-1081402501560130010080Sep-

10. 106480230159013008430Oct-105990220132012007650Nov-
10532021099013006650Dec-10464018066014005620Jan-
11598021069014007020Feb-117620240102015009030Mar-
1183702501290140010050Apr-1188302901620150010890May-
1193103301650130011420Jun-11102303101590140012270Jul-
1187202901560150010720Aug-117710270153014009650Sep-
116320250159015008310Oct-115840250126016007510Nov-
11496024090015006250Dec-11435021066015005370Jan-
12602022057016006970Feb-12792025084015009160Mar-
128430270111016009970Apr-1290403101500170011020May-
1298203601440160011780Jun-12103703301410170012280Jul-
1290503101440160010960Aug-127620300141017009500Sep-
126420280135018008230Oct-125890270108018007420Nov-
12534026084019006630Dec-12443023051018005350Jan-
13610025048020007030Feb-13801027075019009220Mar-
1384302801140200010050Apr-1391103201410210011050May-
1397303801340190011640Jun-13101203601360200012040Jul-
1390803201410200011010Aug-137820310149021009830Sep-
136540300131022008370Oct-13601029098021007490Nov-
13527027077022006530Dec-13538026043023006300Jan-
14621027040020007080Feb-14803028075019009250Mar-
148540300970210010020Apr-1491203401310220510995May-
14957039012602001611436Jun-
141023038012402102212082Jul-
14958035013002302611486Aug-1476803401250220149504Sep-
1468703201210220158635Oct-145930310970230117451Nov-
14526030065024036453Dec-14483029030023015651
Tractor Unit SalesTractor Unit
SalesMonthNASAEurPacChinaWorldJan-
1057025056021201592Feb-1061127060023001711Mar-
1063026068024001810Apr-1068427065026301867May-
1065028058026901779Jun-1060027059028001740Jul-
1051226476029001826Aug-1050028064527001695Sep-
1047829065026301681Oct-1045528067025801663Nov-
1040729088824001825Dec-1036028085023001720Jan-
1157132062025001761Feb-1165035076027502035Mar-

11. 1174039074227002142Apr-1184044078028002340May-
1183047069029002280Jun-1176049072130002271Jul-
1168148168031202154Aug-1167046071130502146Sep-
1164046069529002085Oct-1162044065026001970Nov-
1157043668025001936Dec-1153342065724001850Jan-
12620510610250102000Feb-12792590680250122324Mar-
12890610730260202510Apr-12960600820270222672May-
121040620810290202780Jun-121032640807310242813Jul-
121006590760340202716Aug-12910600720320312581Sep-
12803670660313302476Oct-12730630630290372317Nov-
12699710603280322324Dec-12647570570260332080Jan-
13730650500287352202Feb-13930680590290502540Mar-
131160724620300632867Apr-131510730730310683348May-
131650760740330703550Jun-131490800720340823432Jul-
131460840670350803400Aug-131390830610341903261Sep-
1313608205993301003209Oct-1313408105603201023132Nov-
1312408275503001103027Dec-1311037505202901142777Jan-
1412507804802001112821Feb-1415508055232101213209Mar-
1418208305602201233553Apr-1420108905702301203820May-
1422309305902531304133Jun-1424909806002701364476Jul-
14244010025802801344436Aug-1423349705702501324256Sep-
1421909605502301374067Oct-1420809305302201303890Nov-
1420509205171901393816Dec-1420049024901901313717
Industry Mower Total SalesIndustry Mower Total
SalesMonthNASAEurPacWorldJan-
106000057113091104574662Feb-
107718461117679111196585Mar-
1077885658227591068102369Apr-
1086190778279661237116171May-
1096117886278951313126210Jun-
1097143882305661176129768Jul-
1084757848294441359116409Aug-
1079804735283641238110141Sep-
106480065728393121595065Oct-
105930759524444115485500Nov-
105215755318000126271972Dec-

12. 104504946212453138659349Jan-
115862755312778144373401Feb-
117620061518214151596545Mar-
1182871658238891373108791Apr-
1184904784294551442116584May-
1193100846294641215124625Jun-
1193000838274141333122585Jul-
1183048763273681415112594Aug-
1174854694273211296104164Sep-
116076962529444140292241Oct-
115561961023774146881470Nov-
114815557117308135167386Dec-
114264751212941138957489Jan-
125788553710962150970892Feb-
127764759515273140294917Mar-
1281845659205561524104583Apr-
1286095756267861574115211May-
1291776878248281468118949Jun-
12100680825247371560127801Jul-
1286190756248281441113216Aug-
127188771425179154599325Sep-
126000065124545166786863Oct-
125556664319286169877193Nov-
125085761915273181068558Dec-
12425965489107173153982Jan-
13580955818571188769135Feb-
137556661413158184591182Mar-
1380286622196551923102486Apr-
1385140727251791981113027May-
1390093826231031810115832Jun-
1395472783242861942122482Jul-
1387308681247371961114686Aug-
1374476646266072000103729Sep-
136169862522982207587381Oct-
135723861716897201976771Nov-
135067358713750209567105Dec-

13. 13512385917818215061797Jan-
14597125637547185269673Feb-
147796157113889174394165Mar-
1483725625183021892104544Apr-
1490297723251922037118250May-
1491143848247061887118583Jun-
1499320792253061944127363Jul-
1493922745270832170123919Aug-
1473143739260422037101961Sep-
146669966726304201895688Oct-
145647666022558207281766Nov-
145106862514773218268648Dec-14468936086977203556510
Industry Tractor Total SalesIndustry Tractor Total
SalesMonthNASAEurPacChinaWorldJan-
108143984509198727815483Feb-
10859210515310109028316325Mar-
10863010166071112728517129Apr-
10894710275856120928817327May-
10844210575273122128616278Jun-
10750010195315132728715448Jul-
1061459777170132428915905Aug-
10588210575926126829014422Sep-
10559510866075120929314258Oct-
10523310456321116829514061Nov-
10449410788381112729815378Dec-
10391310297944108530114272Jan-
11593811725688118530614289Feb-
11663312737037128630216530Mar-
11732714236981128630317320Apr-
11807716127500134630718842May-
11783017286571138830917826Jun-
11710318156990144931217669Jul-
11623917766667149031516487Aug-
11603616856762144931816250Sep-
11566416796635139432115692Oct-
11534516186311125631514844Nov-

14. 11483115646476121431814402Dec-
11445415226250117132013716Jan-
12529918355922120833314597Feb-
12652921156667121431316836Mar-
12712022027228125660618412Apr-
12761921518200131157119852May-
12838722147941141555620513Jun-
12811022787921152052620355Jul-
12775221007677167551319716Aug-
12689421287200158476918575Sep-
12601523676735152775017394Oct-
12536822116495142273216226Nov-
12496424836061136671415587Dec-
12444419865816126269814207Jan-
13500022575051137371414394Feb-
136284235360821436106317218Mar-
137785245763271478126419310Apr-
139934251776041512133322901May-
1310645261277891642155624244Jun-
139491274973471667173922993Jul-
139182288769791733170222483Aug-
138528283364891700191521465Sep-
138293278963161642208321123Oct-
138221276558331576212820523Nov-
137470274657891493229219789Dec-
136509253455911450224518329Jan-
147267263551061010229218311Feb-
148807270354741045244920477Mar-
1410168279560221106240022489Apr-
1411044299760641150235323607May-
1412120313163441244260025439Jun-
1413459331165931357265327374Jul-
1413048339063041421260026764Aug-
1412275327760641263254925428Sep-
1411347323257891173245323995Oct-
1410667313156991128251723142Nov-

15. 141045930875604974254122666Dec-
141008230305444979245321989
Unit Production CostsUnit Production
CostsMonthTractorMowerJan-10$1,750$50Feb-
10$1,755$50Mar-10$1,763$51Apr-10$1,770$51May-
10$1,778$51Jun-10$1,785$51Jul-10$1,792$51Aug-
10$1,795$51Sep-10$1,801$52Oct-10$1,804$52Nov-
10$1,810$52Dec-10$1,813$52Jan-11$1,835$55Feb-
11$1,841$55Mar-11$1,848$55Apr-11$1,854$55May-
11$1,860$56Jun-11$1,866$56Jul-11$1,872$56Aug-
11$1,878$56Sep-11$1,885$56Oct-11$1,892$57Nov-
11$1,897$57Dec-11$1,903$57Jan-12$1,925$59Feb-
12$1,931$59Mar-12$1,938$59Apr-12$1,944$59May-
12$1,950$59Jun-12$1,956$60Jul-12$1,963$60Aug-
12$1,969$60Sep-12$1,976$60Oct-12$1,983$60Nov-
12$1,990$61Dec-12$1,996$61Jan-13$1,940$59Feb-
13$1,946$59Mar-13$1,952$59Apr-13$1,958$59May-
13$1,964$60Jun-13$1,970$60Jul-13$1,976$60Aug-
13$1,983$60Sep-13$1,990$60Oct-13$1,996$60Nov-
13$2,012$61Dec-13$2,008$61Jan-14$2,073$63Feb-
14$2,077$63Mar-14$2,081$63Apr-14$2,086$63May-
14$2,092$63Jun-14$2,098$63Jul-14$2,104$64Aug-
14$2,110$64Sep-14$2,116$64Oct-14$2,122$64Nov-
14$2,129$64Dec-14$2,135$64
Operating & Interest ExpensesOperating and Interest
ExpensesMonthAdministrativeDepreciationInterestJan-
10$633,073$140,467$7,244Feb-10$607,904$165,636$7,679Mar-
10$630,687$142,853$6,887Apr-
10$613,401$160,139$6,917May-
10$607,664$165,876$8,316Jun-10$632,967$140,573$7,428Jul-
10$609,604$163,936$8,737Aug-
10$607,749$165,791$7,054Sep-10$603,367$170,173$8,862Oct-
10$629,083$144,457$8,488Nov-
10$611,995$161,545$7,049Dec-10$625,712$147,828$8,807Jan-
11$656,123$175,447$7,430Feb-11$652,679$178,891$6,791Mar-
11$655,521$176,049$8,013Apr-

16. 11$676,581$154,989$8,979May-
11$676,581$154,989$7,484Jun-11$656,440$175,130$7,858Jul-
11$661,969$169,601$7,424Aug-
11$677,212$154,358$6,848Sep-11$653,545$178,025$6,751Oct-
11$657,388$174,182$8,160Nov-
11$672,475$159,095$7,898Dec-11$656,325$175,245$8,953Jan-
12$723,594$226,526$9,443Feb-12$759,042$191,078$8,464Mar-
12$749,187$200,933$10,264Apr-
12$751,499$198,621$8,547May-
12$741,452$208,668$8,578Jun-12$729,122$220,998$9,519Jul-
12$734,783$215,337$9,343Aug-
12$748,208$201,912$8,448Sep-12$738,186$211,934$9,957Oct-
12$759,403$190,717$9,738Nov-
12$726,183$223,937$9,785Dec-12$757,037$193,083$8,191Jan-
13$672,232$179,138$9,914Feb-13$665,023$186,347$9,954Mar-
13$667,657$183,713$10,859Apr-
13$654,198$197,172$9,730May-
13$659,435$191,935$10,430Jun-
13$661,190$190,180$10,222Jul-
13$647,321$204,049$10,102Aug-
13$666,743$184,627$10,610Sep-
13$678,705$172,665$9,374Oct-
13$658,990$192,380$10,830Nov-
13$656,221$195,149$9,017Dec-
13$676,934$174,436$10,423Jan-
14$641,571$210,589$9,985Feb-14$634,973$217,187$9,766Mar-
14$662,054$190,106$11,148Apr-
14$654,962$197,198$9,339May-
14$645,579$206,581$9,468Jun-14$658,112$194,048$10,324Jul-
14$637,711$214,449$9,737Aug-
14$638,317$213,843$9,290Sep-14$651,996$200,164$9,213Oct-
14$630,766$221,394$10,143Nov-
14$645,095$207,065$10,383Dec-14$637,807$214,353$9,059
On-Time DeliveryOn-Time DeliveryMonthNumber of
deliveriesNumber On TimePercentJan-101086106998.4%Feb-
101101108098.1%Mar-101116108997.6%Apr-

17. 101216119998.6%May-101183116898.7%Jun-
101176116098.6%Jul-101198118198.6%Aug-
101205118998.7%Sep-101223121098.9%Oct-
101209119498.8%Nov-101198118098.5%Dec-
101243122398.4%Jan-111220120198.4%Feb-
111241122498.6%Mar-111237121798.4%Apr-
111258124298.7%May-111262124698.7%Jun-
111227121298.8%Jul-111243122798.7%Aug-
111281126498.7%Sep-111272125498.6%Oct-
111295127898.7%Nov-111298128198.7%Dec-
111318129698.3%Jan-121281126498.7%Feb-
121320130498.8%Mar-121352133498.7%Apr-
121336132098.8%May-121291127698.8%Jun-
121342132698.8%Jul-121352133798.9%Aug-
121377136098.8%Sep-121385136898.8%Oct-
121356133898.7%Nov-121362134698.8%Dec-
121349133398.8%Jan-131386137198.9%Feb-
131358134298.8%Mar-131371135698.9%Apr-
131362134899.0%May-131350133899.1%Jun-
131381136698.9%Jul-131392137899.0%Aug-
131371135999.1%Sep-131402138798.9%Oct-
131384137099.0%Nov-131399137798.4%Dec-
131369135799.1%Jan-141401139099.2%Feb-
141388137699.1%Mar-141395138599.3%Apr-
141412140199.2%May-141403139299.2%Jun-
141415140299.1%Jul-141426141599.2%Aug-
141431142099.2%Sep-141445142698.7%Oct-
141425141499.2%Nov-141413140399.3%Dec-
141456142798.0%
Defects After DeliveryDefects After DeliveryDefects per
million items received from
suppliersMonth20102011201220132014January81282882468257
1February810832836695575March813847818692547April82383
9825686542May832832804673532June848840812681496July83
7849806696472August831857798688460September8278398046
71441October838842713645445November826828705617438Dec

18. ember819816686603436
Time to Pay SuppliersTime to Pay SuppliersMonthWorking
DaysJan-108.32Feb-108.28Mar-108.29Apr-108.32May-
108.36Jun-108.35Jul-108.34Aug-108.32Sep-108.36Oct-
108.33Nov-108.32Dec-108.29Jan-117.89Feb-117.65Mar-
117.58Apr-117.53May-117.48Jun-117.45Jul-117.36Aug-
117.35Sep-117.32Oct-117.3Nov-117.27Dec-117.25Jan-
127.22Feb-127.21Mar-127.22Apr-127.29May-127.25Jun-
127.23Jul-127.28Aug-127.25Sep-127.24Oct-127.26Nov-
127.21Dec-127.23Jan-137.24Feb-137.19Mar-137.21Apr-
137.23May-137.22Jun-137.19Jul-137.17Aug-137.15Sep-
137.16Oct-137.16Nov-137.15Dec-137.14Jan-147.12Feb-
147.11Mar-147.11Apr-147.11May-147.11Jun-147.12Jul-
147.08Aug-147.09Sep-147.09Oct-147.04Nov-147.06Dec-147.08
Response TimeResponse times to customer service callsQ1
2013Q2 2013Q3 2013Q4 2013Q1 2014Q2 2014Q3 2014Q4
20144.364.333.714.442.753.451.672.555.424.732.524.073.241.9
52.582.305.501.632.695.114.352.773.471.042.794.213.473.495.
581.833.121.595.556.895.124.692.893.721.003.113.650.921.006
.365.094.595.404.058.025.273.448.262.331.173.903.384.000.90
6.041.911.691.464.491.263.343.852.538.933.881.902.060.904.9
25.002.396.853.392.954.492.313.553.523.265.695.144.693.572.
713.525.204.683.050.983.343.411.651.255.133.595.912.343.593
.313.582.185.291.071.002.804.032.792.964.351.002.861.823.06
2.392.093.782.462.184.443.742.401.634.282.872.074.554.876.1
11.592.404.470.902.902.136.764.783.054.441.944.872.585.242.
844.131.504.963.903.115.504.081.257.175.584.413.320.902.474
.043.435.703.113.402.203.524.245.092.981.001.083.153.523.18
1.887.664.653.403.634.872.310.904.254.652.662.041.863.971.0
01.355.080.904.994.371.903.855.901.624.402.013.762.476.072.
811.091.871.641.343.123.201.001.764.601.036.408.052.125.831
.005.583.522.313.684.914.323.941.194.924.141.993.925.063.61
2.473.792.634.133.974.133.264.023.895.863.272.431.003.344.2
62.636.880.902.862.343.513.281.704.471.712.243.832.532.413.
242.304.186.390.901.794.142.473.255.354.736.573.872.702.654
.025.202.332.654.182.463.613.212.035.283.672.368.823.840.90

19. 3.853.624.334.733.643.352.433.382.204.124.641.055.625.501.5
44.384.571.402.652.670.906.510.902.872.992.493.424.166.400.
903.692.114.192.673.970.903.212.871.732.863.034.331.263.513
.557.453.523.121.901.956.165.955.933.492.231.862.092.706.40
2.055.523.035.352.411.031.761.008.214.967.465.112.982.952.6
43.632.524.854.846.460.907.424.495.343.995.572.885.611.013.
791.623.742.594.820.953.634.562.481.105.631.343.183.053.875
.672.714.50
Employee SatisfactionEmployee Satisfaction ResultsAverages
using a 5 point scaleDesign &Sales & QuarterProductionSample
sizeManagerSample sizeAdministrationSample sizeTotalSample
size1st Q-112.861003.81103.51303.071402nd Q-
112.911003.76103.38303.071403rd Q-
112.841003.86103.45303.041404th Q-
112.831003.48103.61303.041401st Q-
122.911003.75203.37303.111502nd Q-
122.941003.92203.53303.191503rd Q-
122.861003.89203.47303.121504th Q-
122.831003.58203.66303.101501st Q-
132.951003.82203.71403.251602nd Q-
133.011004.01203.53403.271603rd Q-
133.031003.92203.62403.291604th Q-
132.961003.84203.48403.201601st Q-
143.051003.92203.52403.281602nd Q-
143.121004.00203.37403.291603rd Q-
143.061003.93203.46403.271604th Q-
143.021003.70203.59403.25160
EnginesEngine Production TimeSampleProduction Time
(min)165.1262.3360.4458.7558.1656.9757.0856.5955.11054.31
153.71253.21352.81452.51552.11651.81751.51851.31950.92050
.52150.22250.02349.72449.52549.32649.42749.12849.02948.83
048.53148.33248.23348.13447.93547.73647.63747.43847.13946
.94046.84146.74246.64346.54446.54546.24646.34746.04845.84
945.75045.6
Transmission CostsUnit Tractor Transmission
CostsCurrentProcess AProcess

20. B$242.00$242.00$292.00$176.00$275.00$321.00$286.00$199.0
0$314.00$269.00$219.00$242.00$327.00$273.00$278.00$264.0
0$265.00$300.00$296.00$435.00$301.00$333.00$285.00$286.0
0$242.00$384.00$315.00$288.00$387.00$300.00$314.00$299.0
0$304.00$302.00$145.00$300.00$335.00$266.00$351.00$242.0
0$216.00$277.00$281.00$331.00$284.00$289.00$247.00$276.0
0$259.00$280.00$312.00$322.00$267.00$273.00$209.00$210.0
0$281.00$282.00$391.00$303.00$304.00$297.00$306.00$391.0
0$346.00$312.00$236.00$230.00$287.00$383.00$332.00$306.0
0$299.00$301.00$312.00$300.00$277.00$295.00$278.00$336.0
0$288.00$303.00$217.00$313.00$315.00$274.00$286.00$321.0
0$339.00$338.00
Blade WeightBlade Weight
SampleWeight14.8824.9235.0244.9755.0064.9974.8685.0795.04
104.87114.77125.14135.04145.00154.88164.91175.09184.97194
.98205.07215.03225.12235.08244.86255.11264.92275.18284.93
295.12305.08314.75324.99335.00344.91355.18364.95374.63384
.89395.11405.05415.03425.02434.96445.04454.93465.06475.07
485.00495.03505.00514.95524.99535.02544.90555.10565.01574
.84585.01594.88604.97614.97625.06635.06645.04654.87665.00
675.03685.02695.02705.06715.21725.09734.97745.01754.90764
.89774.93785.16795.02805.01815.10825.03835.07844.92855.08
864.96874.74884.91895.12905.00914.93924.88934.88944.81955
.16965.03974.87985.09994.941005.081014.971025.231035.1210
45.091055.121064.931074.791085.101095.121104.861115.0011
24.941134.951144.951154.871165.091174.941185.011195.0412
05.051215.051224.971234.961244.961254.991265.041274.9112
85.191295.031304.991315.121324.971334.881345.071355.0113
64.891374.951385.091395.091404.891414.931424.851435.0314
44.921455.091464.991474.921484.871494.901505.021515.2115
25.021534.915451555.161565.031574.961585.041594.981605.0
71615.021625.081634.851644.91654.971665.091674.891684.87
1695.011704.971715.871725.331735.111745.071754.931764.99
1775.041785.141795.091805.061814.851824.931835.041845.09
1855.071864.991875.011884.881894.931905.11914.941924.881
934.891944.891954.851964.821975.021984.91994.732005.0420

21. 15.072024.812035.042045.032055.012065.142075.122084.8920
94.912104.972114.982125.012135.012145.092154.932165.0421
75.112185.072194.952204.862215.132224.952235.222244.8122
54.912264.952274.942284.812295.112304.812314.972325.0723
35.032344.812354.952364.892375.082384.932394.992404.9424
15.132425.022435.072444.822455.032464.852474.892484.8224
95.182505.022515.052524.882535.082544.982555.022564.9925
75.022585.032595.022605.072614.952624.952634.942645.1226
55.082664.912674.962684.962694.942705.192714.912725.0127
34.932745.052754.962764.922774.952785.082794.972805.0428
14.942824.982835.032845.052854.912865.092875.212884.8728
95.022904.812914.962925.062934.862944.962954.992964.9429
75.062984.952995.023005.013015.043025.013035.023045.0330
55.183065.083075.143084.923094.973104.923115.143124.9231
35.033144.983154.763164.943174.923184.913194.963205.0232
15.133225.133234.923244.983254.893264.883275.113285.1132
95.083305.033314.943324.883334.913344.863354.893364.9133
74.873384.933395.143404.873414.983424.883434.883445.0134
54.933464.933474.993484.913494.963504.78
Mower TestMower Test Functional
PerformanceSampleObservation12345678910111213141516171
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Employee RetentionEmployee
RetentionYearsPLEYrsEducationCollege
GPAAgeGenderCollege
GradLocal10183.0133FYY10162.7825MYY10183.1526MYN10
183.8624FYY9.6162.5825FYY8.5162.9623MYY8.4173.5635M
YY8.4162.6423MYY8.2183.4332FYY7.9152.7534MNY7.6132.9
528MNY7.5132.5023MNY7.5162.8624MYY7.2152.3823FNY6.
8163.4727FYY6.5163.1026MYY6.3132.9821MNY6.2162.7123
MYN5.9132.9520FNY5.8183.3625MYY5.4162.7524MYN5.117
2.4832MYN4.8142.7628MNY4.7163.1225FYN4.5132.9623MN
Y4.3162.8025MYN4173.5724MYY3.9163.0026FYN3.7162.862
3MYN3.7153.1924MNN3.7163.5023FYN3.5142.8421MNY3.41
63.1324MYN2.5131.7522MNN1.8162.9825MYN1.5152.1322M
NN0.9162.7923FYY0.8183.1526MYN0.7131.8422FNN0.3183.7
924FYN
Shipping CostUnit Shipping Cost
PlantCustomerMowersTractorsSingaporeToronto$1.71$2.03Bir
minghamToronto$1.34$1.78FrankfurtToronto$1.52$1.87Mumba
iToronto$1.67$2.14Kansas
CityToronto$1.36$1.79AucklandToronto$1.86$2.19SantiagoTor
onto$1.49$2.13SingaporeShanghai$1.44$1.78BirminghamShang

28. hai$1.60$2.15FrankfurtShanghai$1.65$2.32MumbaiShanghai$1.
21$1.47Kansas
CityShanghai$1.58$2.13AucklandShanghai$1.18$1.63SantiagoS
hanghai$1.47$2.03SingaporeMexico
City$1.72$2.09BirminghamMexico
City$1.29$1.79FrankfurtMexico City$1.54$2.04MumbaiMexico
City$1.56$2.22Kansas CityMexico
City$1.32$1.76AucklandMexico City$1.50$2.07SantiagoMexico
City$1.22$1.58SingaporeMelbourne$1.43$1.70BirminghamMelb
ourne$1.52$2.06FrankfurtMelbourne$1.73$2.28MumbaiMelbour
ne$1.38$1.63Kansas
CityMelbourne$1.72$2.34AucklandMelbourne$0.91$1.17Santia
goMelbourne$1.49$1.80SingaporeLondon$1.88$2.68Birmingha
mLondon$1.47$1.77FrankfurtLondon$1.37$1.64MumbaiLondon
$1.44$1.82Kansas
CityLondon$1.49$1.86AucklandLondon$1.98$2.60SantiagoLon
don$1.58$2.14SingaporeCaracas$1.50$2.01BirminghamCaracas
$1.37$1.86FrankfurtCaracas$1.59$1.88MumbaiCaracas$1.61$2.
08Kansas
CityCaracas$1.54$1.90AucklandCaracas$1.54$1.98SantiagoCar
acas$1.00$1.26SingaporeAtlanta$1.73$2.35BirminghamAtlanta$
1.02$1.25FrankfurtAtlanta$1.42$1.70MumbaiAtlanta$1.57$2.23
Kansas
CityAtlanta$1.31$1.82AucklandAtlanta$1.74$2.26SantiagoAtla
nta$1.31$1.76
Fixed CostFixed Costs of Capacity Increase or New
ConstructionCurrent PlantsAdditional CapacityCostKansas
City10000$605,000.00Kansas
City20000$985,000.00Santiago5000$381,000.00Santiago10000$
680,000.00Proposed LocationsMaximum
capacityCostAuckland15,000$917,000.00Auckland20,000$1,136
,000.00Birmingham15,000$962,000.00Birmingham20,000$1,18
0,000.00Frankfurt15,000$874,000.00Frankfurt20,000$1,093,000
.00Mumbai15,000$750,000.00Mumbai25,000$959,000.00Singap
ore15,000$839,000.00Singapore20,000$1,058,000.00
Purchasing SurveyPurchasing SurveyDelivery speedPrice

29. levelPrice flexibilityManufacturing imageOverall
serviceSalesforce imageProduct qualityUsage LevelSatisfaction
LevelSize of firmPurchasing StructureIndustryBuying
Type4.10.66.94.72.42.35.2324.200111.836.36.62.548.4434.3110
13.45.25.764.32.78.2485.211122.717.15.91.82.37.8323.9111160
.99.67.83.44.64.5586.800131.93.37.94.82.61.99.7454.411124.62
.49.56.63.54.57.6465.800111.34.26.25.12.82.26.9444.311025.51
.69.44.73.537.6635.4001343.56.563.73.28.7545.411022.41.68.8
4.822.85.8324.300013.92.29.14.632.58.347500122.81.48.13.82.
11.46.6394.411013.71.58.65.72.73.76.738500114.71.39.96.732.
66.8545.900033.429.74.72.71.74.8494.700033.24.15.75.13.62.9
6.2384.401124.91.87.74.33.41.55.9405.600025.31.49.76.13.33.9
6.8545.900134.71.39.96.732.66.855600033.30.98.642.11.86.341
4.500023.40.48.32.51.21.75.2353.30001349.17.13.53.48.4555.2
01032.41.56.74.81.92.57.2363.711015.11.48.74.83.32.63.8494.9
00024.62.17.95.83.42.84.7495.900132.41.56.64.81.92.57.2363.7
11015.21.39.76.13.23.96.7545.800133.52.89.93.53.11.75.4495.4
00134.13.75.95.53.938.4465.1110233.265.33.138433.311012.83
.88.96.93.33.28.253501035.229.35.93.72.44.6606.100033.43.76.
45.73.53.48.4473.811012.417.73.41.71.16.2354.111011.83.37.5
4.52.52.47.6393.611113.645.85.83.72.59.3444.8111240.99.15.4
2.42.67.3465.1001302.16.95.41.12.68.9293.911112.426.44.52.1
2.28.8283.311111.93.47.64.62.62.57.7403.711115.90.99.67.83.4
4.64.5586.700134.92.39.34.53.61.36.2535.9000351.38.64.73.12.
53.7484.8000222.66.53.72.41.78.5383.2111152.59.44.63.71.46.
354600033.11.9104.52.63.23.8554.900133.43.95.65.63.62.39.14
34.711125.80.28.84.532.46.7574.900135.42.1833.81.45.2533.80
0133.70.78.262.12.55.241500022.64.88.253.62.59535.211124.5
4.16.35.94.33.48.8505.511022.82.46.74.92.52.69.2323.711113.8
0.88.72.91.62.15.6393.700012.92.67.772.83.67.7474.201124.94.
47.46.94.649.6626.211025.42.59.65.5437.765600034.31.87.65.4
3.12.54.4465.600132.34.584.73.32.28.750511123.11.99.94.52.6
3.13.8544.800135.11.99.25.83.62.34.5606.100034.11.19.35.52.5
2.77.4475.3001333.85.54.93.42.66364.201121.127.24.71.63.210
403.411113.71.494.52.62.36.8454.900024.22.59.26.23.33.97.35
9600031.64.56.45.332.57.1464.511025.31.78.53.73.51.94.8584.

30. 300032.33.78.35.232.39.1494.811123.65.45.96.24.52.98.4505.4
11125.62.28.23.141.65.3553.900133.62.29.94.82.91.94.9514.90
0035.21.39.14.53.32.77.3605.10013326.66.62.42.78.2414.11101
4.22.49.44.93.22.78.5495.200123.80.88.36.12.22.65.3425.10002
3.32.69.73.32.91.55.2475.1001311.97.14.51.53.19.9393.311114.
51.68.74.63.12.16.8565.100035.51.88.73.83.62.14.9594.500033.
44.65.58.244.46.3475.601121.62.86.16.42.33.88.2414.111012.3
3.77.6532.57.4374.401012.638.562.82.86.8535.611022.53.174.2
2.82.29433.711112.42.98.45.92.72.76.7515.511022.13.57.44.82.
82.37.2364.301012.91.27.36.122.5834411114.32.59.36.33.447.4
606.1000332.87.87.133.87.9494.401124.81.77.64.23.31.45.8395
.500023.14.25.17.83.645.9435.201121.92.754.92.22.58.2363.61
10140.56.74.52.22.1531400110.61.66.450.72.18.4253.411116.1
0.59.24.83.32.87.1605.2001322.85.252.42.78.4383.711013.12.2
6.76.82.62.98.4424.311012.51.8952.236334.40001