276: mgmt 276 statistical inference in management - Study Guide
276: mgmt 276 statistical inference in management - Study Guide 276
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This 23 page Study Guide was uploaded by Morgan Marchetti on Thursday October 23, 2014. The Study Guide belongs to 276 at University of Arizona taught by delaney in Fall. Since its upload, it has received 175 views. For similar materials see mgmt 276 statistical inference in management in Business, management at University of Arizona.
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Date Created: 10/23/14
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by the Desert questionnaire yeu have beer given Does this queetiennaire reuide L At least ene epeneendedLquot uestien 2 At least e e ebleeeedended queefietn An ende anehered rating scale J 44 f u y anehereed rating scale F f393 en frquot 5 6 A rank eptien eeale ajequotgt mr x eementiiGe differenetial eeasle en 1 A chee1k iet L E ZV he p Sufmmated eeniee ea queetiene j a r Please use the fehlewing scales te evaluetee the qeueetienneeiree Ii 2 quotTryeequeetiennaire used preeie and concise Th quot1UE51iD113iFE used appregpriaite natuera anguarge nusitijeirteee eaeuai nor tee fermalj 1g g3 4 e a 1 539rren jfquot gree WEquotetrc1f T P 6 PC PC ff X Pie feel free 10 eemment en yeur reepense or e we exemi es ef your ffeedbaek here V 0a I H 2 H P j 391 vi 39e 9quotquot39 quot39t A I air r 2 K H P 3939l 1 J Lquee eene qjueetieene Slh and elearjl gmmeg 1 1 aH23usnu3Ke iv kS39trengr r V1 igree Weetref gree 39 gree feel free to Gemment en your reeepenee or give e II L 5 I 391 u 39 I quot e 0 e5 l j I I L W 3 jF39h1 In V H J quot ya H H V I i 39 la 0J quotn p uli my 39 39a J ae E an 3 3 Elquot s Hquot H I 39 iJ 9 quot i 5 This questionnaire contained leaded er leai 1 p T E E E V E E V E H E E E i I i traingfy gree Weutrefi gree Pieaee feel free ate comment n year reepeneeii r give e i A I 39 i V V quotquot P I vquot N T an 9 iquot i539quot 39 ansquot 0M gt u 13 quot 4 quot f Ii i 1quota 39i a i39 w i 39 39 R 1 x 2 39 39 e i la 1quot I i 9 is ii i 4 quot L quot vquot 39 9 V J 4 This QUES H DFIi39IEIif E eentaained 39di39DUbEEquotbaiiiTE l39e d er deublie inegatiyei queatiene 1 2 3 4 i15 Sitrengify ea Weatref gr39ee39 Please feel free to eernmenit en your response or giiae Clptiene DHthi539 q u StiiDF1 ltEii E eemeitimee were everiapping er iineeimpiete 1 aai2a 3 4 5 t reeg 39 fgree V53 W39eutref grea reegi gree 0 DLiagrea iF ieeee feel free ta eemmeinai am your reepenae er give examples ef yeur feedback here This quee1iinf1aire contained eummaied queetiene designed in meiaeure a single eenetmet O O no I weiild guess that the eingile eenetruet ef the eiuwmmated quetiene was ill 1 W 239 H FE K 139 IL IiaMH l K 1 0x j lir I ii 39T qj Hr i l i I if quot j P quot5i39 ii A i 1j quot i39iTf39quotii quotV W 391 j E u39 quot4 quotl ail 57 I IL J P 3 M V T hi39ieeqdueietieinnaire appeared pireiieieeienal leaking an attraetiviei py quot4quot A2 i3K P 5 if itreegijrr ree Waeereif ilgzre2 S39 39revegjr fD 5quota39gra39a Z Geneirialiy I would rate thie queetiiermeire Y i e s2 A 3 iif 4 5 quoti Jeai afieer Wear W39eu 39ref aaquotefr m Ei1E Please feel free te eemmernt en yeur rEE j7ifZ1irT lt 7 er give exampiee of your feebeek here E t im i13939IIi E qa ei39ina nein3r iiru maE fnripee rre1rieT 2iE1E 1 1 Il l h J Lrlhquot 39l Eh o 2 Smtisficuli in Lecture 5pr39irg VEV 200 330 Tuesdays 0 Thursdays WrHingv As5ignm3n1 numben T z 1 7 7 n pp ibj1 quot1 Ii quotquot H 0 J 1H mampigEmU J 1 39s F 3yr5I E p n ML 77 ampjEquota quot Eiquot quot39 5 L H 1ELk1hi1y1pw 1 j x aquot391mE L T 5 21 uIi LM Ti 439 j1 IJx 3 394V3939quot d In an i E Tj i quotA T43 F4 PN O I F I 393939 n39 N 0f quotH1 PC I 11 Igt39 quotan Li L P s p 2439 p v T3 v E A iLl uDTL Ti HqLjqAm amp EIqKw L39l 1 L LIL r quot quot a F PX p rt G G u LAW in W E L 1 M W p J 5 v r r F1 ls 5 P T 5 39s39se39 H Wm mg ml L Jr H E g I aw 739 iii M E L quotea quotuf L at s 91 1 H 4 39L quot IF I 1r 1 quot quot5 Fiirwst Initial df Last NBi 391E Varlalzilmv and Means lslirlihuudnsi Wxdrjissneet iHiEI I7IEl BI I IEl39I39IZ 8 til39IZiit I I f f i in M inmrelnent Silriin QIJFIYJ Dr Slmzamme D l iimw wri1iin Assidniuirienl if ii W i 2 Grdiip members names X i yiur name first 1 ii HI39i1quot Cl P0 3IEFEEi quot tiIEEZ F i 3 4i We wiil making up iexdsmiples U fdi 39EiTEif1tdiS tFiibUtiD39f39IS given their descriptions Step 1 Generate examiples Df 2 distiributiidns that have the same mean but different var iabi39ity Describe what they are far example hsieiights of imen in the icIass Draw them and Ilamel them ihere and Ihen answer the 39fdilIldwinig questiidnd four each J F Mean i39fi39i739i Esiziimate Stainidard deiiiaitidins Variance Find raw dddrei For E 2 D ig ilquot39wquot Find raw SCFE for 2 i Find FEW Scdrd for E F 2 in Find raw scare fdir 50 ifjEii3939quot39rEi39ltiiiEI s i if percen tid R u i i ggjrfi 1 0v Ti Gdrydidz Mean U PJ Edtiiirnaten Standard ddviatidn E Viariiaince 1 f74 Find raw score fdr z d 0 P Find raw score fdr p J Find raw SCOTE fair 2 2 2 1 i M u T Find raw El2 iquotE fdr 50 ipercentilez i 25quot ercdntiIe M 9 p P 71 Li J 735quot percentile 0 C L n 39 E i L 39 Q j Step 2 Generate texamtptes at diistributinns that have the ditfertengt nweians but the same variability Eiescribe what they are for example scares an art hiistnry etam 39 Drtawt them and l1abetIthem here and than answerquot the tn owing 1quotIE5ti39l I5 fur each Mean 2 1 Mean by Esttmtate Standatrd dEviattnn E Estimate Standam deviattcrn 1 Vatrianrre Vartance v find raw tscma tar Find raw scare tar z 2 CI 4u 2 O U Ftind raw scare tor Find raw iSGI Z39I2l39 E tr 9 T M 7 Find raw sczote tar Find raw score for J r 5 Find raw 3Cazgre for Find raw more fat 50 percem1titte o 50 percenttle P h Hquot quot q r J I 39rquot aaFquot 25quot percemttile39 W pl k 25 petrtctenttie l Vi 75th perca tlilae 3g E M wl 5m peFEetI39tile tr a J 1 diap 3 Genezrate exaimple5 012 difquotIiereint dis1rii hutiioris that have the same mean asntil same variability Describe what they are for example 5cnr eE cm art histary exam Draw them and Iahei them here and than answer the following questions fur each 395 5 Curve 1 Curve Mean x Mean iEs tiimate Standard deviatiizrn y Esiiiimate Siandai39d 39i j quotv iiEt ii391F 391 Variiame I Variance nd raw score for Find raw SEDFE ifnr Find raw SGUFE fair Esi 1 F ind raw scare far 22 Find raw score far 50 erceintile 25 perireniiie q Yn LTii i S I395 perceintiiei 5392quot i F is K quotLi Finiici raw SGCii39B fur 21 ii Find iraw scours far 2 2 4 Find iaw BEUTE fr 5 percentile 25 percientileiz I T5 perc iitlle iHl 1 1 il Lll iih Lg I5 r i v f V A l W H l P p if TIE SillE39 STA E i 1 vrf xrr I LF 39 d l 7 r13939a Eepe ta efwara39 preeide teargctattiartt tsaraiae auntie afaeaytr rlrepraaa ear Easirtaasr 0 r t 3 4Eere fiat N net amp s39tirtg ate hquetttjihnnairg M nameimrenet 2iItE3 Statis cal IIIieremIIe ill rbt nauement Snriirle 205 Er Suzanne 3IiEIl39IBY Hemrewerk 4 Wer run this mete ahd want to Know the tellewihg infermatienr I Hear might we il ll39ll l F l39tr39E eur meterl ta iherreaee sales In What the lilted and didn t lilte aheuti eitherthe eerwieee prmritded er pfhyeieal preperty ef the metal irteelf Whe terrde te eta here edema demegraphie infermatieh ahd why they eheee thie metel Wha39tEiJ EF in39ferrhatiah think can help us impretre rates er number of euetamere Pleaee eenetruet a queetriahnaire fer the quotSweet Stray Metel by the Deererti Be sure ta feilaw the 15 prrineiplee ef queetietnnaire Gehatruetieh prlevided iin leture The quveetidnhaire elheuld he tyrpedi and leak prrefeeeiehal it eheuld eentain at least 1D 1ueetltiarie and ehuld edhtairi an example at each at the fellewihg types at rquee ene t leeet rhe 39 rrpenendedquot39 queetiieh At least ehe eileeedenided dueetieri Arr entlarlehered if tllflQ eeale 15 fully aneheredi rating eeale E E l39EFlh ljp ti Drnl eeaie J3 eemarhtic diriferehtial eeale p eheelltiet A eummated eeriee f qLlEtlUlquotl5 at least 3 measuring a eihgle eehetruet Fer the eake elf eemrehieme I l39lE il39E eumrnarizedi the pirriheiplree of queatiehniaire C lt tfu tl here and hrevided examplere ef the 5 different types at scales Summary of 15 prrineiplee ef queetieninaire eenetruetien i make eure items match research ebjeetwee Llhderertand yeurl research participants try to quotthinllt lliltie the responders eeneider their eeheilzzilitiee Llee apprrdprriate natural and familiar language Be clear precise and eaheiee iehert questions dehl39lt make them werh any harder than they need the maid iIeading er leaded queetieha rtwdtid deubieebarr el ed queequottiehe Avoid deubl h egathree Be aware ef whetheir l ZuDEll39IeElquot1 tZl39E r eleeedehdeid queetien tie mere ueeful N FtJF39CL39EStiEf1lquotlS with eptierie I rnalte eptiehe ne nseeruerlaiphihg ahdi cempilete all peeihle 10 Ceheider late hf di quotereht fermate far reapeheee See bellow fer ezaarhplee df diffferent ztrmatej V P l Ceheider eummated queetiehe e 12 Ceheider eemplerhent ing yraur qiLlSTiElHlquotI3ll39E with ether ferrma ef data eelerti eh feeue tgreup or direct abeeraatien 3913 Consider the phe homenri out aoouieeeeitoer response and consider aaitirtg the eirnilrar questions in riiif fer erttwa1re i4 39ii39ake queatiorInai r e userfrieno i3r aridquot nounthreatening moat triiendiy oueetionra first then at end now at eosuoile queetilone about you foot in the dioor phehomenoih use oonitingeney questions sparingly can he eorituieing 15 Pilot feieuzibaoit fix piit artahrze fix pilot eto Reeoeot orooeaa of erttpirioali aoprotaoh Examples of a few different formats Endaanohoared ratinr scales a written desoripton on ends of the eoaie itraa 123 45 Dtlregr aa39 Fuilly anchored rating ecaIeai a W39r39i139lEii39i d esoriotion for each point on the scale 39123 4i39 5 S39troitgifjy igrae Natitrof gr1a339 Rank optinone in aeoeridiri g or deaoendi hg order H ttporttttartt P owa 539iE H uE arEJT3E iota tyfsgaera at Z pi3ae539ertt eeigy ort w w rooma Etroagijr 1I3I39E Igf E39E assets to Sea route wot r op T wor fiet area i ei re ton wor f setiool SEHiquotIjEIilquotI C di ffErE39iIquotItiiEiI Create a profile of jyour opinioh n aportrttartt Home ss oeiif Ea ijtititira iEE i ITi j Cozy utrt if5ttt4ti339 T 3 1 g roomy tI irIflf Quiet A i V tt7quotea39 commuttt39ty t arga39r mLt 1f1F mi 2 g Larger i5atfrtnoirtt Gheckrliet ea epttrtntartt house 5i39 i 39ttil fF IIi quotE39 Ceae oitfdastraoreitztirt iut39ea Z Ci39f539 u E1q39 ErE feat wor grriop ftritiri ipat tIr i 3tt 3 arrerr to ea 139 739l 39i39Ei39f 0 roemir fr pif2or39atnt naig ora p 5036 to 39t4uoriE Etfiri f Summatecl Qiueetione aeree of queetieria deaigned t measure o ne trait All of the qiueetiona are iahawererfi and than the reapoheea are added up to giitre you a eihgI e measure for that one eharaoteirietio For extamplet if you iwianteo to know itow muich eomeorie imowe about the hiphep efLi39tuire you might write a little quiz er 10 qLEStiEii iS rand eee hoot many they get rijgitti The rtumber correct on y oLir quiz woiuslio lose the measiure of their hio hoo izoowiedoe Anotheir eztarriple if we were to rheaeure howi eooiaiy iiiberaii eomeorie ie yreu might ask at eeriee of question about titeir poiitira views in this oaae larger numbers would rnear quotmore Iiberal I 39 Pfeasa ttr39ciquota the degree to w io uou tgr39e39a 39rut39tiE ettoii stetetrteriit I quotEtso tttort rtep K1ra t7ttpti5fir atftrut tztirm egrea I 2 u 3 R c rogree 2 iitorrtega 5iioa i39539e 395E ttJrEE i one mtltt i39tttaForta 39tt 39 tt tt t agree I i P 5 rfiaogrfaa 3 jcitioirttona riiotiiitr 52 ttzicto39e tifizgofregerctffiesa tziia ift39t39tf39it391Fri39itt1TItft5quot agree P5 2 3 ii e t 5 oitogrea ii5i39riir I urtt39ortrmt1raerortrarrrt139or t ittO llltlansgement 26 Statilstlical inference in Manegement Spring 2009 Dr Sluzelnlne Delaney Hlelmewerk 1 1l Flleese assess the hemlewerllt trern eur elsss wehslitea wwwigenessrizenleeduicieleneyf Clliek en l emewerk 1 139 Stumbiing en H eppiness 39 This is en etvzeeret frem the test Eitumblingl en H39eepinless by Dsnli lel Gilbert 20055 pages 65 T2 Piease read this excerpt I i3iiirH jJt39itJ39iT HgtIj tt In it Daniel Gilbert discusses sevelrel issues we39ve heen tlking ebeut in elsss in e way that l thinh mekes it rner intuithr end ujndelrsltsnldalsle These tepies ilneluee the l ew ef terge lnumhersl elenstrnets eperstiensl deltinitiens rneesuremenlt end measuremlent errer retlllsbilllity ef instrurnehts end the scientific nnetihlled Fellliewingl the eseerpt there is some infermzaltien smeutl the eiuthler just its glare yeti s sense et when 0r tslllltingl Yeus sen else see e shert vides ef hin 1 hetng interviewed hy the es mledisn Stephen Delbert st htte wwweelbertnsti39enemathssel39bertreijlert 1rie eesfe9235 june2lE ffeienielllgtieert The assi gnn1entssseeist edl wllth this reedinl is tie sulmrnerize the reeIiingl in the fenn ef eenstrueting 5 rnultirple elheiee qiuestlenls based on yeur elvsluetienl ef the main peints Please type y39eLn39 responses to these q7Ltestiensl These ere ldue the first eless sessien after the Spring Break Heliclsy Marsh 24 Marne F Management f Statistical l fllllite in Maneeement Sulrine 209 Dr Suzanne Delaney WV Itemewerk 8 This hemeweiirk will preside practise in finding and manipulating rnaeens raw seeresi z seeres end preihaibilities using the nermel euwe We will assume s thermal euwe for these preihlems end will use the z tehle in Agppendizt 31 en page 7371 ef eiur textheel Fillease shew yiur werk write smell and nest end eirele fyeur fine answer lF er questiens l 15 please essume a distribution with a mean ef 30 end a standard diristien ref C E l Find the area under the curve between seeres Dif 28 cG Start by filling in the esired interrnietien en eurrr 1 t the right ffltfete t tir eits fJ It 3 require rdtquot 39tr Itr en39r jtr39ri tHere quotstept ritdfresl ELr we reentertissdr rir rquotlEr sj H vquot quot9quot r39 If til l quot il J H Find the area under the eu We ibetween scores at 0 34 Start by filling in the desiired inferrnetien err suhre te the right Wattle t rlr errs drstrtjt r sqirir quote sezrifrtliiltierid jirrt these set3t39rt39 efm 9t sue msmerti sedfer titres1 Find the eree under the eunre etweeh sceres et 3 P 36 Sterrl hly filllingi in R desired infrrnetien n siuwe 3 tea the right itfhts tlitir eras d zrern quott reqrrr39rrr reisuitiitiiene 3 f mSE quot smp39n39csfrefs3 we msmertsedfer elites 4 Find the area under the curve fer scores ef 30 end ehieve Sterl by fillling in the desired intermet ien en curve p tn the right 0 Ir EiS l ems d estrrlalt rsg rrrs39 rt 1l5itrr513Itfi339IrLtl L il F A R I r t 5 Find the area under the CLJEWQ hetwe en seeres ref 30 Start by filling in the desired infermetien en ieuirve P tie the rightji fIifets39 the dies trlri quotrlrslj 1rrrs 39ys tr te se EIrfst s rt s 5sers39fer is me mural qf393 3 ms the 3Ei wt fr T i 5 ll I quot B 39 rll r rquot 7 in quotV I 7 l V II J 1 F 39 1 I r 1 2 A ll l use s ll it is 19 397 739 39 l quotI 39 1 39 quot quotI r39 39 2 I 5 E E 39 I X X be i F i 4 I p V I I1 N I q r l S 17 l P quotDH l ii lr E I l i 6 Find the eree under the curve essecieted with e seere tit 33 end blew Start hy fiillling in the desired interimetien en curve B to the right Wade the see reel rsgsitirsgyes tn EttK39fCt1 391iit 1 s scerefer I1 nriw slterre lf33 eedese the s teE Firrr fert in the prebtem yes she Fr39rre39r ng the pensee ler rc rel39r fer is seerte sf 3 rrrr 3910 111 12 13 14 115 Find the area under the ounie Hsieoc ia ed with we aoore of 33 and atotre Start by filling in the desired informratioh on GiLlWE39 to the right 1 F 4 75 an U r PBq i J p quot F t F It 9 i39quotiiJ it Find the area under the EUWE B3506Ei lt d with a score of 29 and above Start by fitting in the desired iniformation on curve 8 to the triighite 1 7 n t I r fiiv 39 J 39 i t I I W xi Find the area under the ourve hetwieenieooares of P H Start ihyrfiltiing iin the desired intorrnetion on CIJWE Q to the right arm the one wt retgtttre you to tettifatiitte oi ehorefer39 the reitu tore rf25i end I quot7 M J t J Jr quot5 39 H h WEE Z 39 quot391 Q Hi i sh we 3 Find the iperoentite rank for a scorer of 2 Start by titling in the desired if information on wet We to to the riigahttt Hrne thee 5rIrrrr3939or we praerem e A f 5quott hite that one wttTre qat39re j i39tTJquottt te et39Ettt te39 1 E v f f f ratw rriIre of 2 ethd ete39 the Zthee F t P X 0 Pq x P p Q H 397 39 run Find the area under the eurtre associated with at eeore of V and aboiret Start by filling in the desired information on curve it to the right EV E 1 V l e bk ii W Find the area under the oLtrre aeeooated iwith a eeore of 32 and above Start by tilting in the dsired information on more 12 to the right W1 F t r E as D mi 1 Y i i trquot Pp 3 X F F w J we 2 A B E at quot39339 ZA 39 F J l f ll A ii til D 3 Find the raw eooirei that tie associated with the 50 peroentite 39W7otte39 rtoieet cttt51tiorrr ere heeeeterji F J Find the 2 raw scores aeeooiated with the middle 68 percent of the euwe Ft1quotottr39 an EiltjiIIt 1tti tTf ere rtecer5r1g pR6 0 it Q 9 7 J5 t Find the raw score that is the 7 pie roe ntile White tea are wt ttquotragutre jtt139tt ta mta eaiacorev hm the ere ertder the rttwe 0 ttstittg the E t tEti39 heeiigpuerdfrj i tsrmeatt erj raw stme mete 3 5to39retZtmhut39ertdeeiettoa E t t 397 n V 739 F I I ll Fer qiueatireria id 30 pieaee use the felliewirig diS r39i3biUti39DiIi1 with a mean cm 20 and a standard eiriatiern at 4039 V d id Find the area tinder the curve fer aeerea at 200 arid above Start ib393 fiiliing iii the desired ihferrriatieh eh Guihiet id tie the right Ziifata ttiir amt cfaaqrift r agtt39ra Editt3t eegJ39 i M i4H 1 Find the area under the iciurvei hetweeri eeeree at 160 E 24i1 Start by fililirtg in the dieaired irifrrriatieri an curve 17 to the right V3 Fate 513 aria iEf J39 JE ragrazire er1i5Eii Eii1iertt jart theea quot arepI39rr39 df rui ttr tzte memd ead rc 5a e ii i V i ii 1 391 L I t a 1 17 18 Find the area under the curve between Saree ef i 280 Start hey titling in the desired inifiermatiein en curve 18 tn the right quot li9 Fin the area LindE39i39 the curve ibE hWEEH eeeirea ef 0 he F Start by fiilirijg in the desired infeirmatrien en eunre 19 tie the right Wquotata39 the are deeaa it require t eiEtttEt WlITi39439Iquoti3939iT fritat Hide eataih7tai7ratr39 we meraettetad39fart 1t55 Find the area urider the euhre between eeerea at 200 and T Start byf iiIlir1ig in the deeired tinfermaitiiern en GUIWE 20 tree the right 5 t397dta this El d LLdTrag ur ra yea ta rfditrut te at ZEfv tf tf f 1tiCIf rrar7e 2339t2i39 dntfme the a i3tI5i39 E Z ti i VV s 0 quott a 6 quot396 T r it i 39quot e J5 E 3 21 Find the area under the eunre aaeeeiated with a aeere ef iQU arid ibelew Start by iiiilirig in the desired in arrhatien err euirve 21 tie the right ififtfata tiitzlr are rrtsiffregu39ira39 re ta t7a EaL1Le e ieascerefer at iquot39tI39w were I90 aatfeae the z teEfe Fen zteif in rihre prteb fame you are rridrhg are painted i39quotr iquote rarer for a serene at 90 i e 7 Li 7 i gt V Na 7 x E I 1 In H 0 iquot 39 t T 39 quot3 w mij E 3393 P 39 It i it 3 it 1 Find the area Linder the were aeeeeiated with a more at 180 and ahewe Start by titling in the desired irifermatien en CLIWE d te the right at W i l39 n va39 i ti 439 hi i at p E re fr K V F t N a E H X W h A U ti 0 4 H i Ii a n i i I 39T 23 Find the area urider the curve aeeeeiated with a scare at 235 and aheire a Start by iiil liing dirt the desired iiniferrnatieh en eu we d te the tight en ll 39 H i p P0 2 at i O p 245 d 28 311 Find the eree under the runre etween eeeree of 192 p W Start by filling in the desired infermatieni en euirve 0 tie the right Wete eie em tuttiquot regui39re yen to eiItri te 1 eetrerefer the drew IE rE395 eff emi392t22fi Find the ipetreeintaiiet renk fer a eeere et k Start by fiilii ng in the desired iinf rrnetiien en curve 25 rte the right free ittifs nrrre wt3391quot require we re irtt39Ctit E1te39 it 536ert3fere raw rt ire 2 i ehduee tiEe e teEt Her 2 tquoti i r 39 tie sim39rr39 re preefem IS i1quotH lI39F i1II39 A Find the area under the eurve eeeecieted with a eeere of p A and above Sterrt by titling in the desired infermatien en euwei 26 to the right 7 Find the area under the curve eseeciete di with e eeere ref L 30 and above Start by filling in the deeired interimatiien en EUiW39E 7 tie the r39ight G7 J i I Find the area under the curve eeeeeiet ed wiitih e eeere of 130 and belewi Start by tililing in the dieeird infern39Ietiien en eurvte 28 te the right i2 J I 39 Find the raw ecere that is the 7 percentilie iH iim Sirriiiar tee prnbllem 15 Rermemtiter fete eitzire 39merm 6 5 e39rei5rtendhrdquot rfeirI39t1r139erti F if N V I J v5 J Find the raw ecere that is the 40 percentile f emem er rcrturcere mert Cr ceremhderdHequotpt39ttiz39err 1 39 y P Y B Hun I I Jr 1 5 J39L39te iitr rnnu 39i39Ly T llttanaglemerit LZTE Statistical inference in Nlanagemenlt Spring Dr Suzalnne Delaney gr Hernewerk 11 7N This is an excerpt train the text Bturnhiihg n Happiness by ljaniel Glilhezrt mes pages as 2l Please read this exeerlpt rt I lrI 7TRP HL lflrK xE39l391llm1jfquotiEtt39 in it Danilel Gileerl discusses several issues WE39VE been talking albeut in cllass in a way that I quotthink n1lailtes it rnere intuititre an urnderstlandabller These tepics inceude the law et large numbers censtructs eperatienal definitions measuremelnt and measurement errer rellliahlilityi cf iristrurnents and the lscielntiticl rnethcd Fellewine the excerpt there is same inferrnatilen asset the autlher just the give grelu a sense ct when is talking Yeti can else see a shert sridee elf him being ilntenriewedl be the celmedilan Stephen Celhert at cellgrrertnatiencemrthe celbertrepertsirideest 39Q235fiunte2 rei ewdanielagriltierir The assignrnent asseciiated with this reading is he surnntarize the reading in the term cf censtructing 5 lntultiple cheice euestlens based en weur eaaluatiien ef the main paints Please type yeur respnses te these euestlens These are due the tirst class sessieri after the SpiFllli39IQ El39EElk Helilday pirareii 2394 7Stumlr39rg en Happr ness p lane Gllbert Measuring Right The first prernise alien sueclessruily rrrleasuring suejecrive lerrperiencesa like happiiriesser Jcestenrer sarisfacltreln er ernpleyee tuirillmenrquotlfl is sernething that any carpenter celuld tell yeu lrnperliect heels are a reel pain but they sure heat peunding nails wfith yeur teeth The na39ture ef slubjectiire experience suggests there will never he a happyemeter a perfectly rleliiahle instrument that allews an eeserrer te rneasure with eernlplet e accuracy the l s lharacteristgics elf anether persen39s sushjctiae experience so that the measurement can he l2Elquot Elquotll recerdedr indr cernparedl with anether is If we dernand that level ef perlectien frem eulrtees1 then we betterquot pack up the eye trackers brain scanners and celer swatches anti cede the study elf subjectiael errperience te the pests wfhe did a nicejelb with it fer the iirst few theusand years But if we de that then it is ernlyr ifair that we hand there the study ctquot alrnest everything else as well Chrenlcnteters therrnrernetersj balrentetelra sipectremeters and every ether device that scientists use he rrleasure the ebjects ef their interest are lnliperfectr Ereryene let them iiillltr39m dl39il B39ampE same degree at errer inte the ehservatierrs it alllews which is why gcaernlrnents and uniraersitlies pay GDSGEl llE stunts ctquot rnene39r each yrear fer the siglhtl5r mere perfect irersien ef each And it we are purging eurselires ct alt things that affelrd us enly irnperiectapprexin1atlier39is U39ftil1E iFLJftht then we need te discard net erily psychlelgy39 and the physiical sciences but law eeenemicsr and histery as well in shert it we adhere te the stalndard cf perfectien in all cur endfieatrers we are left with nething hut rnatl1eniaiics and the White Alhurmr Se ntayhe we justr1eled te accept a leit cf fuzainess and step cemlrplailnlirigl The secend prernizse is that et all the awed measurles e1isubj ectiire esperilence that we can take the 39 he nest realtime repert ef the attelnthre indi39uirhial is the least tlawevdi There are malny ether wxays te measure helpp iness eta ceulrse and serne elf them appsalrte be much mere rigelreus sclerttiticg and ehjrectliae than a perserrs wn claims Fer eltarnlple lellelctrernyregraphyr alllews us te nteasure the electricall signals prediueced by the striated museles cf the facert such as the cerrugaterquot supercliliia which turrews eulr brews when we eitperienee serriething ulnpleasanst er the zygernaticus rnajer which pulls cur rrieuths up teward eulr ears when we sn39iille Physilegrahy allllews us te measure the electrederrnal lrespirlatery and cardiac actiirity ef the aiutenerriic HJEWKDUS strstem all ef which change when we experiences streng ernetienrs W lElelctreenee2phalelraplwgl pesiltren emissien tezrnegraplhyr and rnagnetic resenanee ilmagirig allew us te V Po easure elleetrical aetiait1r and bleed fllew in dlifierelnt regiens cf the brain such as the left and right plreirental i celrter which tend te he actiire when we are esperliencing lpesritiae and negative entetiertsj respectiirei3r Eaten e elleelk earn lee a useful dleuiee fer rneastiring hlappiness eeause startled peeple tend the blink rnere slewly when tlhiey are feeling hiappy than when they are feeling fearful er anztiireus 1 Seientists whe rely en the henest realtime reperts ef attentive ineiiuiduals eften feel the need the deterid it that eheiee by reminding us tht these reperts rerrelate strengly with ether nieasures ef happiness But in a P sens taheyhre get it baeitward After all the enly reasen why we take any elf these beelily errelnltsefrem musele meaemlent te cerehral bleed tlewas i39ndi ees ef happaineslsi is that pelepte teiii us they are if reruwerlryene eliairned te feel raging anger er thithi lblaelt depressien when their s1rgematite muselle eerntracted their eye ibliinh slewed and the left anterier brain zregien filed with bleed then we weulel have te reirise eur interpretatiens ef these rphylslielegieal changes and take them as ilndieels ef unhappiness insteadl If we want te knew hew a pErE39Dfril feels we must begin ably aeltnewledging the fact that there is ene and reniy ene ebsenrer stationed at the eritical peinl ef uiew She may net allwayrs remiemleer what she felt befiere and she ntaiyr net always be aware ef what she is feeling right new We may be puzzfled by her reperts skeptical ef her rnre n1err and werriedi abeut her ahitlityr te use lrganguage as we die But when all eur hand wringing s leer we rnust iadrnit that she is the en r persen whe has even the st39ght39est ehanee ef describing quotthe view ifrern in here which is why her elaims serve as the geldl stanclardl against which all ether measures are nieasuired We will have greater eenficlenee in her C ii3iITlE when they jihe with what ether less priiriileged ebseruers tell us when we feel eenfident that she e39rauates lher esperrlienee against the sarne baekgrezund that we de when her bedr ideas what meat ether bediies tie when they egtper ienee what she is elairning te e1riperienee and se en But even when all ef these uilarieusi indiees eaf happiness derretail nieely we eannet be perfectly sure that we knew the truth ahaut her inner werld b ean hew eirer be sure that we have eerne as class as ebseruers ever get and that haste be geed eneugh Measuring Ct zen The third prentise is that irnperfeetiens in rnelasurernenat are always a prel eern but they are a dietrastatineg preblent enly when we dent reeegnize them if we have a deep serateh en eur eyeglasses and cienquott lmew it we rnay erreneeuslsy eernelude that a small creek has epened in the fiabriac ef space and is felilawning use if f wherever we ge E ut if we are eeginizant ef the serateh we ean ele eur best tie faster it eat ef eur ehsertratiiens rernining eurisehres that what ieelks like a rip in space is really just a flaw in the demise we are using te elzsenre it What eani seientists de te see threughquot39 the flaws inherent in reperts eff subteetive rarperienees The answer lies in a phenmenen that statisticians call the few at ilarge nurnbers Many ef us have a rnisstaken idea abeut large numbers namely that they are like smali numbers enily leiigger o such we ettpeet them its rle mere ef what small numbers de hut net te de anything dfffelrent Se fer iinstanee we knew that twe neurens swapping eleetrreehemiieal signals aeress their azarens and dendrites eannet pessibly he eenseieus Nerve cells are sirnpile devices less cernplex than waikitetaikiies frem Sears and they tie ene siimple thing nalmrelly reaet te the ehen391ieais that reach them by releasing ehemieals let their ewn If we bli thely ge en te assume that ten hillien at these simple d Etr7iCEB earn enlyr cie ten hillien sirnple things we weuld never guess that i liiil l39i5 ef them can exhibit at preperty that twe ten erten theusalnd eanrnet Censeieusness is preaisely this sert ef ernergent ereeerty a iphenentenen that arises in part as a result ef the sheer number ef intereenneetiens anrang neurens in the human brain and that dees net exist in sin ef the parts er in the intereenneetien ef just a few Ctuantum physics effers a sirnilar lessen We knew that SlLJl3EIilE3i39t iiiC particles lhaire the strange and eharrning abi39lfity te exist in twe places at since and if we assfume that anyrthinig eernpesed ef these partieles rnust behatre likewise we sheuii39ld erest all sews te be in all pessible barns at the sarne time Whiieh they ehtrierusy are net lbeeause finredness is anether ene ef these preperties that Elf39IquotEl gB5 frem the interaetien ef a terrifbly large nurnher efterribl3r tiny parts that de net themselves have it in sheet rnere is net just rnere it is EiiiI 39lquot39lEv tii l lE 3 1Z f39 E quot39 than less as The magie ef large numbers werks aleng with the laws ef prrebahiillity te r39ernaedy rnany ef the preblems asseeiiatet39i with the impetrfeet nteasurernent ef subjeeTtiitre experienee Yeu knew that 0 a fair eein is flifppee en several occasions it shoud come up heads about hallf the time As such it you have nothing better to do on a Tuesday evening I invite you to meet me at the Grafton Street Publ in Harvard Square and piay an endearingliy mindless game called Splitting the Tab with Dan Here39s how it lwolrlrs We flip a coin l call heads you call tai ls and the loser pays the good barkeep Paul for our beers each time Now it we flipped the coin four tilmes and on three of them you would undoubtedly chalk it u to bad luck on your part and cfhallenge me to darts But if we flipped the coin four lmillion times and I won on three million of thiem then you and your associates would probably send out for a large order of tar and feathers Why3939 ecause even if you don39t know the first thing about probability theory you have a very keen intuition that when numbers are small little irnperiections like astray gust of wind or a dab of perspiratlion on a finger can influence the loutcome of a coin flip But when numbers are large such intperiecltions stop rnatterilng There may have been a dollop of sweat on the coin on a few of the fllips and there may have been a wayward puff of air on a few others and these imperFections inight well account for the fact that the coin came up heads once more than expectedl when we flipped it four times But what are the odds that these imperfections could have caused the coin to come up heads a million more tirnes than ertpeoteld lnfinitesima your intuition tells you and your in39tuition is spot on The odds are as gigse to infinitesimal as things on earth get without disappearing altogether This same logic can be applied to the problem of subgjectlive eltxperience Suppose we were to give a pair of volunteers a pair of experienoes that were meant to induce happinesssay by giving a mi lion dollars to one of them and the gift oi a srnall caliber revolver to the other We then ask each volunteer to tell us how happy he or she is The nouveau riche volunteer says she is ecstatic and the armed volunteer says he is mildly pleased though perhaps not quite as pleased as one ought to nquotiEkE39 an arnied volunteer is it possible that the two are actually having the sarne subjectilve ernotional experiences but describing them diifferently Yes The new millionaire may be demonstrating politeness rather than joy Or perhaps the new pistol owner is etperiencing ecstasy but because he recently shook the hand of God near the Great Barrier eef is describing his ecstasy as mere satisfaction These problerms are real problems signi cant problems and we K ould be foolish to conclude on the basis of these two reports that happiness is not as it were a warm gun rut if we gave away a miiifion pistois and a rniilionl enlveiopels of molney and if 90 percent of the people who got new money claimed to be happier than Eilll percent of the people who got new weapons the odds that we are being deceiived by the idiosyncrasies of verbal descri ptions become very srnailil iindeed 3in1iarly if a person tells us that she is happier with today39s bananacream pie than with yestlerdayis coconutcrearn pie we may rightfuly worryl that she is misrelmembering her prior ex perienlce But if this were i happen over and over again with hundreds or thousands of people some of whom tasted the coconutcream pie beiore the banana cream pie and some of whom tasted it after we woud have good reason to suspect that different pies really do give rise to difierent iexperiences one of which is more pieasant than the other after all what are the odds that everjyone misremernbers loananacream pie as better and coconutcream pie as worse than they reallily were The fundarnental problem in the science of experience is that if either the languaesquisihing hypothesis or the eiperiencestretchingl hypothesis is correct then everyone of us may have a different mapping of what we experience onto what we sayand because subjective experiences can be shared only by saying the true nature of those experiences can never be perilectly rneasured in other words if the ettperien ce and description scales are calibrated a bit differently for every person who uses them then it is inipolssibie for scienlirais to clornpare the iciairns ofiwo peopie Tquothat39s a problem But the problem isn39t with the word contpare it39s with the word two Two is too small a nurnber and when it becomes two hundred or two thousand the different caibrations of different individuals begin to cancel one arnother out If the workers at the factory that makes all the world39s tape measures rulers and yiardstilcirs got sloshed at a holliday party and startedl turning out millions of slightly differentsized measuring instruments we would nt feel confident that a dinosaur was larger than a turnip if you measured one and I rneasured the other After all we may have used piclltlled rulers gut if humdreds of people with hundreds of rulers stepped up to one of these lobjects and tools its rneasurements we could average those measuremeents and feell reasonably oonfident that a tyra2nlnosaurus is indee bigger than a root vegetable A er all what are the odds that all the people who measured the dinosaur just se happened te have used stretched rulers and that all the peeple whe measured the turnip just see happened te have used suished rulers Yes it is pelssihle and the adds earl he ealeulated urite preeiselv lziiut l will spare we the math and premise jeu that they are se slender thaEl t writing them dewn weuld endangerthe werd s supple ef zeroes The leettern line is this The attentive persen39s henest real tirne repert is an imperfect EppF IttllTiEtltIlIll i et o her subjective erperieinee but it is the engv game in town When a fruit salad a lever er a jazz his is just tee imperfect fer eurtasties we step eating Kissing and listening But the law ef large numhersi suggests that when a imaeasurement is tee imperfect for em tastes we sheuld net step measuring Quite the eppesitewe sheuld measure again and again until niggling impertestiens yield ts the enslaught at data These subatemie particles that like he he everywhere at enee seem te eansel eut ene anethlelrts hehavier se that the large eengiemeratien ef particles that we sell sews ears and French Canadians stray ertastlv where we put them By the same llegie the careful selleetien sit a large number ef eaperienlial reperte allews the imperfeetie ns ef ene te eansell eut the i mpelrfectiens ef anether Ne indivi dua39s repert may be taken as an unimpeashahle and perfectly ealilgrated indexquot ef his esperiieheeanet veurs net minebut we be eenfidsent that if we ask eneugh peeple the same guliest ien the average answer will lee a reughhr aeeurate index at the average reisperienee The ssiensel ef happiness reuires that we pllay the edrds and thus the liinfermat ien it prevides us is always at same risk ef hsing wreng But if veu want te bet against it then flip that sein ens mere time get eut yeur wallet and telll Paul te matte mine a C uinness Onward One efthe mast anneviing sengs in the eften anneving history at ppular rnusir leegins with this line quotFeelings nething mere than feelingsquot l wines when I hear it because it always strikes me as reughilv equivalent te starting a hymn with quotJesus nething mere than Jesmus Nething mere than feelings What eeuld he mere impertant than f eeIings F Sure war and peace may came te mind but are war and peace important tern any reasen ether than the feelliangs they preduee If war didn t cause pain and anguish if peaee didn39t previde fer delights heth transcendental and Carnal would either let them matter tie us at all War peaee art merley marriage hirth death disease religiilnetihese are just a few ef the Really Big Tepies ever which eeeatns ef bleed and ink have been spilled but they are real7r hig tepiss fer ens reasen alenet Each is a pewerful seuree ef human emetien If they didn39t make us feet uplifted desperate thankful and lhepeliess we weuld keep all that ink and lzuleed rte eurselves rs Plate asked quotAre these things geed fer any ether reasen eiseept that they end in pleasure and get rid f and avert pain Are you leaking le any ether standard but pleasure and pain when veu eall them end Indeed feelins denquottiust matter thev are what rnatterring rheans We weuld EDtllteCt any creature that feels pain when burned and pleasure when fed te sail burning and eating sand and gees respeeti vejr just as we 39weuild expeet an asestes ereature with ne digestive traet te find sueh designatiens aarbitrarv Nleral philesephers have tried fer eenturies te find seme ether way he define end and had but nene has ever senvinsed the rest er rne We eannet say that semething is geed unless we ean say what it is geed far and if we examine all the many elbjects and experiences that eur species calls geed and ash vv39ha tthe1r39 are geed fer the answer is elear By and large they are geed fer making us feel hlappgt Given the impertanse at feelings it weuld be nice he he ahle te say preeis ely what they are and hew ene might measure them Are we have seen we ean t due that with the kind ef preeisien that scientists sevet Nlenetheless if th methedelegieal and seneeptual teels that seiense has develepedl die net allew us te measure the feelings ef a single intiivildual with pinpeinit aeeluraey they at least allew us te ge stumleling in the dark with pickled rulers te measure deaens et individuals again and again The prelsl elm facing us is a iflieult ene but it is tee impertant te ignere Whv de we as eften fail he knew what will make us happv in the future Seienee setters same intriguing answers te this questien and new that we have a sense ef the prehlem and a general methed fer shelving it we are readyr te inspect them 39F39 Ee Errrftfitr excerpt Heirs is sarne inifannatidin about this auther Edueatien Uniuersity at CZeiarade at Denver BA Psi3reherl egy tig ti Prinsetan Uniis e rsity Ph D Sasial Pisyeheivdgzy IQBE aeademie Hitstery assistant Firetesser Uiniversiitjy at Tezsas r Austin 1985 W9 ssseeiate Pr39efesser Uiniiuersityr efiTesas at Austin 19901 c FquotFIZiIfESEEIii39 Uniiverszity at Texas at Jaustxin 1l39El9511996 Hrsfesser Itanrard Ulniuersitgr quot19982 5 Ferd Visiting Pretesser at Behavieraii Ssier1ee iUniunersi ty sf Csi39iiElEIQU Seiheei at Business 2003 Haniard Celliegex F39rdtesser Hanrard University EDGE H39CI IZiiF S BA tram Unieeirrsity at Celerade at Deineetr sumrna eurn lauds 1931 Outstanding Graduate Award Uniueirsiity at Edldrade at Denver 1931 Nieiil P R Fahrien Award tar Eseeiienee in sysheiargy Unieersi ty sfCedrade at veneer 1981 Prinnzzetdni Llniuei sity Merit Prize 1981 132 1953 F39hi Beta Iltaipsa quot tEi8quoti ihlatidriaii Seierue FUU3i391E a139iDn Pf dEt tv f lil Feildw 1981E4 iF erter Ogden Jaeebus Feliawship Prineeten University 1198485 RE39F39lquotI39Ii39LIlfquotIIi392 Dieksdn CentenniaI Enddwed Teaching iFeiIdwship LIniirersi39t5r at Texas at Austin 1quotQSas President s Asrseeiates Teaching Essellerinee Award University ef Texas at Austin IFQQD91 Njatianal Institute at Meintal Health Research Scientist Iiieueteprneint Award quot1991Q6 Arneriseain jEfE h U39DFgi Eal Asvseeiiatien istinguished Seientitis Award tar an Eanlyr Career Centinihutienr ta Psyeheesg3r 1992 7 Feililiew Center fer asdiuaneerd Studyr in the Behauiiearai Seienees 1991Q2 eiiliew Seeietig fer Persrei1aiiity39 and Seeial F39s3reheaQ3r 1996 T Feiildw Ameriean iFsyehdIdgieaI IE39tSE39DEii tt irll quotl939QTquot Feiliiew Sdeiety at Expeiriimeintai SesiaI Ptsyehaeiegyr 13993 James MeKeen Gattell Awe rd 11999 Jiathin Sirndn Guggenheim Memeriail Feundatien Fevliewship I999 Arneriean i3iquotIiiIiZiE Dphi GBii Sseiety FeIdwshIip 1999 Phi Beta EKaprpta Teraehiing Prize Hanrard Uniiversity 1999 13th Mast Frequenitiy MEi ItiEiii39IEd Centrihutdr ts Seeial iPsysheegy Flellaw Amerisain Psyehelegieal Secietgr 2003 Authiidr at 2 f the quotequot7e 20 Masxt Cited Artistes in JPSEP tram iE7i652Ji1i quot quotquotiuiIiswantingquot named by M milli i Dietidnaryr as the 3th rndst pepular new ward at EUU4 Manned ens at HEIquot39uquotEl39d Uniee rsi39ty39s quotF auerite iF refessarsquot by the 392Iasses at ZCIDE 20015 8 2003 Harvard Geiilegse Prrdfessarsniip 2005E fil The Heyat Sdeietyr Generat Beak Prise tar Stiumhiling an Happiness EDD DiEiiquotIIEquot Award fer Olutstanrdinrg Centributidns ta Seeial FquotS t3hEt Q3F Fdundatien fer P39ersenaiIity and Seeial Psyeihixeizieggr EDD Eiesteds ta the arnerieain aeaderny starts 8 Seieneesii Ei
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