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![should the do?
this model. Should any
other led'l Which ones'? Why'.'
red the undcrgraduate9, +-ts Thirteen stu
businr-ss p.ogramat Rollins Collegc 2 years ago.
The following icates what their grade-point
averiiges (GPAs) bc.ing in the program for
2 1,ears and whlt each student scored on the SAT
2;100) when he or she was in higli
school.
grades
Ist a meaningful relationship between
AT scorcs'l [l'a studcnt scores a 1 200 on
the SAT. rvhat do you think his or hcr GPA lvill be'l
What ahout a student who scores 2400?
STIIDEh{T SAT SCORE GPA STT]DENT SAT SCORE GPA
DrscussloN QUESTIONS AND PROBLEMS 147
(d) If there are no tourists at all, explain the pre-
Major Business Other Business
(b)
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q.ur.hr,r, L3oDtscusstoN QUESIoNS AND PROBLEMS 145C.docx
- 1. : q.ur.hr,"r, L/*3o DtscusstoN QUESIoNS AND PROBLEMS 145 CATEGORY Puhhc Private Pri ato Pril atcr Private Privirte Prlvatd Private 20.200 10,.100 4t, I (X) 100 100
- 2. -14 cosr ($) MI'DIAN SAT TEAM OBPAVGERA r 620 1 610 I tt.l0 19ti0 1 930 2 t30 2010 1 590 1720 t]10 B-5 68 8 72' 89 4.02 4.78 3.75
- 4. 0.265 0.238 0.23,+ 0.3 rn 0.324 0.335 0.317 0.332 0.325 0.337 0.31 0 0.296 0.317 Baltimore 0rioles Boston Rerl Sor Chicago White Sox Cleveland Indians Detoit Tigers
- 5. Kansas City Royals Los Angeies Angels IV{innesota Twins New York Yankecs Oakland Athletics Seattle Mariners Tampa Ray Rays Teras Rangers Toronto Blue Jays 3.90 7l.2 0.247 0.31 1 4.10 134 0.260 0..i 15 93 69 12" 1 00 3 r .{i00 66 94 't5 90
- 6. 93 't3 619 691 32" I 00 't S: +:! h ZtltZ. the total payroli for the New l'ork Yankees was almost $200 million, whilc the total payroll fbr the Oakland Athletics (a team known fbr using base- ball analytics or sabermetrics) was about $55 million, lc:ss than one-third o{ the Yankees payroll. In thc fol- lowing table. you q,ill see the payrolls (in millions) and thc total rumb.:r ol- victories I'or the baseball tcams in thc American l-eague in the 20l2 soason. Devclop a regression nrodel to predict the total rtum- ber of victories based on tht: payroll. Use the model to predict the number of victones tor a team with a pay- roll oi ti79 million. Based on the results of the com- puter output, discuss the relationship betwecn payroll and victories. (a) Dc-vclop a rcgrcssion modcl that could bc ttscd to predict the nunrber of based on the ERA. ii08 0,273 0.33:t '716 0.245 0.309 (c) (d)
- 7. (b) Develop a r prcdict the scored. Deveiop a predict the ting aver Develt.rp a 1 2 3 4 5 6 7 8 9 it) 11, that could be used to ies based on the runs that could be used to
- 8. ies based on the bat- could be used to TEAM PAYROLL ($MTLLIONS) NUMBEROF VICTORIES prcdict number of victories based on the on- base (e) of the four models is bener tbr pre<licting the r of victories? (t) Find the best multiple regression rnodel to pre- dict the nurnber of wins. Use any combination of the variables to tind the best nrodel. 4-32 The closing stock price for each o1' two stocks (DJIA) was also over this same time MONTH D.IIA Baltimore Orioles Boston Red Sox Chicago White Sox Cleveiand Inciians Detroit Tigers
- 9. Kansas City Royals Los Angele s Angels Minncsota Ts,ins Ncrv York Yankees 0akland Athletics Seattle Mariners Tampa Bay Rays Texas Rangers Toronto Blue Jays 81 .4 113.2 96.9 78.1 132.3 60.9 154"5 94.1 198.0
- 11. 73 .7 .-1 .-1 .1-ll Thc number of t,ictories (W), earned flrn average (ERA), runs scored (R), batting (AVG), and on-base ntage ( each team in the scason are providcd in the following t ERA is one mcasure of the effective tching staff, and a lower statistics are measures oI effecti hisher numbers arc number is 11,168 11.150 11,1ti6 1i,381 11,679 2.463 1 t,6)1 12.26t)
- 12. I2..15.1 13.063 48.3 17.0 17.9 47.8 32.4 3t.'1 3 r.9 36.6 -tt./ 3U.7 39.5 /1 f 4-3.3 39.4 .10.1 12.1
- 13. 45.2 each of these. Quantitative Analysis for Managemerrt, Twelfth Edition, by Barry Render, Ralph M. Stair, Michael E. Hanna, and Trevor S. Hale. Published by Prentice Hall. Copyright @ 201 5 by Pearson Education, lnc. was recordctl ovcr a f 2-month period. The cl,rs- ing plicc for the D{w Jones industrial Avcrage r:iod. These American .in ihc Oo 4,u,&arv t/.- 2 31 4 -)(y' flu; {tre quuho.J tn+d< r.t fiut'ch.gyU CHAPTER4 . REGRESSION MODELS Use the data in Problcm 4-22 and develop a regrcs- sion modci to pledict sclling pricc bascd on the squarc tootage and numher of bcdrooms. Use this to predict the selling price of a 2.000-square-I'oot l.rouse with three bedroorns. Compare this rnodel with the models in Problem 4-22. Should the num- ber of bedroorns be included in the rnodel'J Whv or why not? MPG HORSEPOWE,R WEIGHT hv not'.'
- 14. / 1-24,),Jsc thr data in Problem 4-22 and develop a rcgres- (----""-' sion rnodel to predict selling pricc based on the- square fotrt.age, number of bedrooms, and age. Use this to predict the selling price of a 10-year-old. 37 37 3.+ t< -1t 30 fa 26 26 25 22 20 2t 18 I8
- 16. 139 1.980 1,191 2.199 2.404 2.61I' 3,236 2.606 2.-580 ) <r17 I Ql':r ) .a^ 1/ 3.248 2.8t2 3,382 3.t97 4,380 4,036
- 17. 2.000-sq uarc-1 oot l-rousc wi th thret bcdrooms. 4-25 The total e l'actors. l-wo ol'these {actors are the number ol'beds in thc hospital and thc number of admissions. I)ata wcre collected on l4 hospitals. as shrrvn in the tbl- lowing table: NT-IMBER HO,SPTIAL OFBEDS ADMISSIONS TOTALEXPENSES (100s) (N{ILLIONS) I 2 -1 4 -5 6 1 8 () 10 11
- 20. 45 6 99 12 1l l-5 2l 63 Find thc bcst rcgression modcl to predict the lotal cxpcnscs ol'a hospital. Discuss the accuracy of tl-ris n.rodel. Should both variables be included in the rnodel? Why or why not? A sample of 20 automobiles wa,s taken. and the miles per gallon (MPG). horsepower. and lotal *'eight were recorded. Dcvelop a linear regression model to predict MPG, using horsepower as the only independent variable. Develop anorher rnodel with wei-cht as the independent variahle. Which of these two models is better? Explain. 4-27 Use thc data in Problent -tr-26 to develop a multiple lincar legression modcl. How docs this conrparc with each of the rnodels in Ilroblem 4-26'l 4-28 Use the data in Problem 4-26 to lintl the best qua- dratic regression modcl. (There is morc than onc to consider.) How docs this compare to the rnodels in Problems 4-26 and 1-27?
- 21. 4-29 A sample of nirrc public universities and nint prilatc- lrniversities was taken. The total cost tbr the year (in- cluding room and board) and the median SAT score (maximum total is 2400) at each school were recorded. It was felt that schools with higher rnedian SAT scores would have a better reputation and would charge more tuition as a result of that. The data are in the follow- ing tablc. Use regression to help answer thc lollow- ing qucstions bascd on this samplc data. Do schools with higher SAT scores charge more in tuition and fees'l Are private schools more expensivc than pub- lic schools when SAT scores are taken into consider- ation? Discuss how accurate you believe these results are using infomration related to the regression models. CATEGORY TOTAL COST ($) MEDIAN SAT -$: a-26 MPG HORSEPOWER WEIGHT 1,84,1 r.998 1.152 Puhlic Public Public Public
- 22. Puhlic Public Pubtic Public 21.700 l -5,600 16,900 1-5,400 23,1 00 21,,100 16.500 23,500 r 990 I 620 1 810 1 540 15,10 I 600
- 23. l 560 189t) 44 40 67 50 62 (Continuetl. ott trcxt pdga) Quantitative Analysis for Management, Twelfth Edition, by Barry Render, Ralph M. Stair, Michael E. Hanna, and Trevor S. Hale. Published by prentice Hail. Copyright @ 201 5 by Pearson Education, lnc. I Do q&ohq4t ?- eA The coellicient ol correlatiou computcd 0"68, a 300-mile trip that took hirn out of town -5 days. what is thc expected atnount that ld claim as '! submittcd a rsement rcquest ltlr
- 24. should the do? this model. Should any other led'l Which ones'? Why'.' red the undcrgraduate9, +-ts Thirteen stu businr-ss p.ogramat Rollins Collegc 2 years ago. The following icates what their grade-point averiiges (GPAs) bc.ing in the program for 2 1,ears and whlt each student scored on the SAT 2;100) when he or she was in higli school. grades Ist a meaningful relationship between AT scorcs'l [l'a studcnt scores a 1 200 on the SAT. rvhat do you think his or hcr GPA lvill be'l What ahout a student who scores 2400? STIIDEh{T SAT SCORE GPA STT]DENT SAT SCORE GPA DrscussloN QUESTIONS AND PROBLEMS 147 (d) If there are no tourists at all, explain the pre- Major Business Other Business (b) (c) A
- 25. R C D E F G 1267 2.90 l lll 2.93 1755 3.00 2070 3.45 1824 -1.66 l 170 2.88 1245 2.15 H T J K L M 1.{-13 2.5-l 2181 7.22 r 503 1 .99 1839 2.15
- 26. 2121 3.90 1098 1.60 in Washington, D.C., dur- is believed to be heavily tied visitrn-s the city. During the data have been obtained: OF (a) Using a computer, develop a regression model that could be uscd to predict starting salary based on GPA and major. (b) Use this model to predict the starting sa1ary fbr a busincss maior with a GPA of 3.0. (c) What does the model say about the starting sal- ary fbr a business major compared to a nonbusi- ness maior? (d) Do you believe this model is usetul in predicting the starting salary? Justify your answer. using in- ,-.- lomation provided in thc computer output. ( * +-:Z Jhe loll.rwing data cive the sclling price. squarev---z footage. number of bedrooms, and age of houses that have sold in a neighborhood in the past 6 months. Develop three regr:ession models to pre- dict the sclling pricc based upon cach of thc other tactors individually. Which of these is bcst? $:4-19
- 27. ing the to the past I 2 years^ I'EAR SELLING SQUARE PRICE($) FOOTAGE BEDROOMS AGE (,EARS)RIDERS}ilP (100,000s) 84.000 L670 2 t I 7 15 79,000 1.339 2 212t0 91 .500 1.7 t2 3 3 I 6 13 r 20,000 1,840 3 4 I 4 15-. i -' 127.500 2,300 3 s .j t4 25' / 132,soo 2.234 3(r' 15 21u r ' l45.ooo 2.311 3 7 16 2o , uo,uuu 2,3i't 3 8 12 2t' ,r,,uuu 2,736 -+ 9 I 4 2' ,ur,uuu 2,500 3 l o 20 44 ,72.5t)o 2.500 1 I I I 5 34 l74,ooo 2,4'tg 3 12 7 1' ,r,.uuu 2,400 3 (a) Ptot these data and delermine whether a linear 177,500 3,121 4
- 28. model is reasonable. 184,000 2,.500 3 (b) Develop a regression model. 195.500 1.062 :+ (c) What is expected ridcrship il" 10 million iourists 195,000 2,854 3 visit the city? Quantitative Analysis for Manageircn, Twelfth Edition, by Barry Render, Ralph M. Stair, Michael E. Hanna, and Trevor S. Hale. Published by Prentice Hall. 30 25 30 40 l8 30 19 7 It) 1 3 3 I
- 29. 0 2 10 3 Ior the data in output indicatos to dcvelop a regression model 4- 19. Exnlain what tlris ' graduation. The starting (GPA), and major (busi- S 5gl s46,ooo '.'r 3.5 Other Business Copyright @ 201 5 by Pearson Education, lnc. (a) If Thomas Williams returns exam GPA model. salary firr stu-
- 30. fiom a local unil'ersity s36,500 2.9 Other SAI.,ARY GPA Major $42.000 s3 1,500 1.4 /. I