421-prac-e2-sol - Pram{Ce fir-933 51mg QEWmax‘)‘...

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Unformatted text preview: Pram {Ce _ fir-933»; ' 51mg QEWmax‘; )‘ i‘ {"x C: in ‘73“; -<’?‘?‘1~W~‘\§> K!” "x Gib-i { ".292?" ' w?” 1. Suppose your waking time for 3' in the 2.5 minand standard deviation 1 min.,-w variable Y with mean 5 *3??? "» 0,3, Ci\’k‘€.($}§"'; 9Q? Am? {-{m‘éigfl niorningis a random variabie With mean ' hiie your waiting timé in the evéni'ng 'is .a' random" min and standard-“deviation 1.5 min. I ' ' ' - a. (824+.4 pts.) What is the :xficféifiizéud Standard I X+Y? __ 3 3. "A" .. .~ 1’5 34%“? '?7"'N>K. NWT/.355?” f‘” 7‘53 “ 9W2.) ._i ;:_3 ' '2.” ' '-_:2- -- WW; “f- {534 *-* 5"“ deviation 0! your totai' fitfie _ i: xvii-113+ I). (III—£444 ptSJI-yorug {aka _:' Each and événing-fof what, _; I I- I I I is. tin: mean and stafida‘m flewat'iqnfifyo:1ri_§:qtglSwaifingstime (X; +331; +29; 34713621?» ,3. .'+ X5 + 1%)?” - '- 2; gig-ll Ski:d:or-t_zpenefs, 275% Sififfidifiifen .' I _._n10<_!gla;i_d 75% a.chain-{apiygn-quéi,£Letjffl - " ' ' "' 3 'X yi'i‘hg 15 the shaftfdi‘iyéfififikiégii. a. (5_pts.} Juséiry'ifié-Bimmgalmgaarér X' _ _ and-Whafi iii-"the'pmbabiiity}Df-f’lsiiciQQSs” -j ' __ .- _ ._ - r - -'-:: . . "1.3- - 10:13::x'fl::::'§;‘:i~'_-_'.. at: Edge???) .§.<:>_I¥f"- I ' '1 r " £13 4&ng afawmaim-fir}?5:5;49392-‘vé Fax; .tw‘i - 73; 3 0‘33. a -':-'3 (what aré independent trials; what ES 3”$i3i;£:e_$s’7 «aw-.z,§::-$i$fii:égkag-f.' new. ‘5‘1'3‘7932 ' mi. <_ 5nwe.éifiifffi'fii‘iijf . V’E‘ST'5ff-flifliPitfin ;¢:?*%‘<%-ic$é i_:.i:e::x 'ifléMV-ig ~- gmw aimssés Id 2,.- (5.pcs.-)-:Using'theattaéhed-ifi?’1¢7‘3"fi“’fitePM23) I I .- _ - M I ammgxsxax " f-‘é “9%” Q9 m—~a,-:v;_' r3“, :_:_._ . ' 1. %-'——; m MM” W >. ' 3' 7‘71.” :4 egéfiéqifmic-653%0453:3311:':'__,;._ 6.2}:5'5‘ if '__.v3-J<X‘>;?>)gf " N orinal 'Iapp'roximat-ip'n to Bifigggiial? Why? _' ‘Wiiat afe'the Inegixx: and standard deviation-of- '03¢-rxe'-r.I'xs’ififii-“m \i “- (9* :f’3‘1";75.) . .976. :i _ j e. :(9_—.I.4_~§—j5_ripts.) afi'fiachejd' {able to find the probafiiiitygthafi :amcag next pnrchgéers' - .3 . alt-WM?- choose-31:56 --C}1AIN—di"iv§3n_mbdel. ngjnpgjte the.:Séing-fituipébi-ifiyi means. of _ ' - '1' 'thé'B'intéx-xiiaiiffgm gig-(131;); tqsixdigitfig 1 : : : .I: 3;- :CGhSfiRations handiiadlby student Aha: I I H involve .prqgramé'ggyinfiax errors, and Lei; X'defithé'-t.he:';1umber .wi'thji's'uch errors in a random sampxepr 100 stiidégthgngujtafionfih -- . . . . . . . - . - _ ._ _ _ _ ' .X353Bihozhiairandmfi variable? .}V};at gm; its QarA_-wei_pse the - 1 ' " ' ' b; ptsJ What is'thé {approximate} probability. :X- at; most 75? AC-ieas't :2 .QC ix 1G :_j-::SEI-__::Z§Tfiwm ax - 'ojghpu'jter center é -_ E; S.) " 151mm: :1 box mnmins 3 red, 3 blue and 4 grew balls- Om? bill; i5 pitiked 4-3133” With “epmcemem- ' f’bg the number ofréd. baiis our. of 31 picked onesA '- _ ' ' _ A. {'2 ptfiL) :Justifyithe use of Binomial model for “Fiat-fare 1:1 and p '3 ._ .bp"1\_ .gflimc ‘3‘ I I 3:36,“? {i'gk'tflv GU“? .- SA-LC ((35% :3?“ ' I“ I- . I I.- .w_.. :- : :3: fl 3; Lt; - b. 1333- tile i)f0babiiiby that, 3 out {if balls at? SW3! Uée the Binomial fornmlaf ' - ..'\?‘£~,t><f~%i53£ 3M”? '9‘. 3(5) . .: - -' - i - :---;--':i - {Rt-351 aim} as“)1-1.":7.‘ "” '90-“?gé' - (:3?9:323::..;.::.:_..;57'" :rf'f'::-::D ptsf'MoCejsS'D-f thanlifact_ufing glassware,- giasS'sié'mS atel_s_ealed by. heating e'm' in a flame, frag tempemcmeorthgaame varies .a bit; 'Ifim'niéan-andistagidard devi'atign gfthis tempgratjfigxI';fiea;si1t'éd_7ifiidgé'grees of Celsius are 52482afiii.-5,73te§pgctively. A manager 3 asks for reSyiiigs- in_.déglrégs; Fahrenheit. The canversiqn Qf jX j’ijfitjd. dé'gréesr‘ahrenheit is given. . by . ' .4 -:- _ ' ' 3'.:-';;.f'-:-' . r ' " ‘2; 0f ‘1’: the Ifemperatu're :iihé Falfiéxxliiééfi "' '. - '3 311.31~131-1;-“-2__ ;___3. 3.3,- .-;.2 -- - . -'::::-";i;'-'-:'-3 493". x {Y7 . . ‘4. _Cl_1ihhoodilead poisoning is 8-.:Efiinfl-hfifiknCé'fitefflf'111%:Eg 30m? fihiid En:- . eight%$1a high: 1:11.09. :Eeaci 1mm. In: a-riandamly chosen-group 95-290.sinkdrexy'what 151$: Prfmabllfiljfifé? Hat3m05.§-239%2S1flf¢fif? 1' highj-béood mad. Vié‘vei? Ese Lhe Normaiapproximatim'ta Eiillons.1a_if{-ifi}- $089913“; Iifié-I_:[Ee€fii.mili_. . _ _ . . I'3sgiF7'I".i1'36' }2_ ‘573 %9‘3'. " 15‘ ' 3 Um?» ' fl“) {WUJAELLN LUK 56"“: ' -. '-‘.\ AKIN-“i” a A ‘23 ’& .0“ a: F? -:*‘-"‘-‘- h 1’ -..... '2- - 9,1— -'\ A“ n...»— m 3‘-'-:3:-.':3.<.mn - to has 6c- .Pc at: V”). .. .- i I I 11:} :3} Explain. bdttiing machine _ ‘ at the amount of fiil dispensed b _: _a'.3..(¢3p'¢s_) Find the probability amt' h}: {5: ' Sam- tbs of fit! tmzasured 1 I -. number. '- idea-3a: = ' I; “m if” :2: 13:2. H ‘ t . _ . - _ _ ' . the bottom of page : I'Dl'" ';§I€l§:0:€:n ta jlifitify the norruaiity, or did You use PrGPO"-1_t'_°9-(I.3._(§ee - - discharges a mean of]; In 25 Houngie's per bottle. It. has be ne- is :iorjfinaliy distributed with a z: a bottle coiltdifi-flxhn 24.8 auntés. “‘T 7% (“i/3 > . en_ observed .6 mince. pk “9 5116‘} bfiififléfi is sciefifiéd- ‘om-‘tih'efintpfit’hf the! —- ' caczl" -1"‘wd.th¢ win bale-3'33 than 24.3 ; )EHGGS. I :.-:._ 'f.;..3§.;':( Vifi+2 .le153)! observatigfisjisfiould- wfiiihgiixdél ifijifihew jsampie, 1f We Wish )5 - - - mailer-than 24-8 with probability 105? Pf)? <;j24.-8)-jdis§tjreasas to 0 as Hm '3c'f3 iibéérvatirms- '- (use "the fact,- riff-- 'decrgaseg .aI-picture would alga}: ilp-Efm . fl -6“$§g)7% a .' {1. Mi a» - . .. «- (KS _ _- . J . . - _ - _ . z. (k 7 _ y. 2H3» - - - ,, -- Maw-cw. my,» .- --" ' - . ' u '3’ %"z"c'&'¥€i‘32a- ("1 “- ' ' ' -——- v . me central c‘a-1(3§-1ti:;;;nkfi and c. used the n9rm2L_ My 01' X . 131d you use . \/ Jud-r N\C\\ (“amé'am - {I l um {Calaxég 741% (""9" $31.0“ B ': -:' ' .MS _ _ I .: . “a”: - gm "mfifir‘k €413“ n J w ‘1 “A -) 3 . Lumu.pwmmwgm_ - _ H i: if; “3 (4pts):i._.¥V-haf z critic-al'izaiue in the $a'mple _._-j'3- “3,,_:_’s,:¢:a.u.et 0 _ confidm1<.té'in—_ . tel-Ival £0: :1 should be '-usejd'eto 'obain 86% confidmiqg -Iev291;'_ (0 mg; $22“ Lg; . 7 Determine-the conrfidenéég 'Ieirei IQ; Sfimplé - goi’x'QQ $931642 W - '3 _ The Eifeigi'me-of n_cé'r_tai11.bafitery jgnqrmglfifidistributed with ' '3. 11111311 1111111301” 6 hours and a_.sta'11darri_ deviation of13g_hauzp_'1‘herc are four I .such-batteriefinapatikage. __ ' '. . " .' ._ I j _. _Wha_t'is the probabifity that {he average fifetinie oft-he four batteries ' . exceeds ’f’hours?‘ ' ' " I ' ' ' -' 281101113? '- ' . . - 3" tic: i 1:13??- -<;1ami_>.t'eis~111117101111 3111;131:1111-1111 3111;. p1 _ _ "Raga find the hiimériml; 111m: .‘jejfifz-‘aj 111:: which} 1:21;? __ 1);) In} 9:97;- - - -";:-.: "' ' . Whatistheprobabifitythat-11%iioiéal-iifetdme:(ifthe-battéfieg win excéefi .- . 311111915111135511111131; - «wxw‘ImhMua-u. g. . 'f . 11' .- {1383 gave the: value of the-sampie'mean E_':.:1_3?I-ar_xd:the ’r” ’ -= .A (:oifae machine is Iregulated so that the amount of coffee dispensed is ram-1min. and normain-diisig-Rn-x-tmi. ; A randomisarn-ple of 16 cups; had an average of 194 ouncésimd standard de-Viatiofi 0.24. (5pts)a- Obtain a 95% (iafifidfcnfim interval (two‘sidezd) far the true ameuntg (population mean) {if coiféedfiSpIéhSedL .. - I ' ' ' I '- I 1'5 (is? M —_; 3:.”3r-2LC3_;§-.{g. 71% u. - '{gghgxgmga ' Theflgféfifiibs {in 'fimnhts) of 3. random Sample of 320' i 5'7" d "I V ‘- T r I I I I _,_ _ Standade evmtxon 6‘2:- 21. Irmd the 90%;:93319511304 gamma: for 3a-'iifet.irzxé 0f 3- Singie film-selected randomly from thispgpfulafifirigfir’e imaké the assmnption that; izfetamesare noun-any distributed, 3;" ' ' ' I I I (1.843 "5 _ 1m fig; _ iii?!» at etuis'hmlfazam- _ f-n :JQQGapsuiejs were tested and pra'ducmi ' _ 3379.8 parceut, with astgmdarid'de ' A drug mauufanlfiter ciaiximgi flat; the mean potent pepc'em, A randnmxmnp'in-o ' ' ' y of mm M- i'ils nutibigticfi was 80' _ _ _ a sample-mean of- _Y_iati_au_a~= ,8 percent. wast um hypptfzegis _ - '_ 1.; {415:5 )“Cfifij‘i‘y' b" . #0:!!éa-uxés as in a" fur-the :i'eé’e}: it 5 u reject. Ha;;4._m+_.,3.1;m..exeap, '- . --;,_2-_:..2_.;¥_.:5 Mpg; 4a.mgfl-gwegigigjgg3:1::1:I“ fa, 3:5 @493;- jmag'p as the Bugging} gn'x'arjasypgfiug gq'ijfize' gimp i x 79.3 '3 :f:_:_. '3 fi¢fiigfgi:gdflj i i i I i i i % 1 i 1 i i i 1‘ 1L- siiicon (tunicm of catch of 32’. 1:111:30me saiecm appmpriaic hypmimsm were icstéd using, Evii’NI’i‘AB, ram: maiput. ' - ' - Mew-p Si, Hm: I SE (ngearz fininhli: n 'Siinym, 32 _. 3.8228'_ 9.335 -' ' 9‘05? :1. - WM: arc' the nppmprigié{hyfiéihcscs in ibis sitéiafifin? _/\)—I—\j-CA\U€.9> - - “45-52 0.33 A cumin type of iron would wumin 0.8-5 gm of‘siiicon pic: :09 gm {85%}. d specimens was determined, and the lting in (he accnmpzmying Z P» Value: _!3_. '_ What {:onciusién-xmui'd be approfiriaié Iféf;’i§'3ig§1ificmlcc lévfii of .95”? 3 MK The __._..- __...r.- &. ...
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This note was uploaded on 07/25/2008 for the course STT 421 taught by Professor Nane during the Summer '08 term at Michigan State University.

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421-prac-e2-sol - Pram{Ce fir-933 51mg QEWmax‘)‘...

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