Let Y1 < Y2 < · · · < Y8 be the order statistics of eight

:l~ • ex. Gr~~ o.h\s.-<3m \o -the~~-eclctto. H~..- ~\Il.otShe ~rc:i n ct.\fe ...h'CC.he horlw .s.OV\.~l indicA\t'e c.\ecs-u'sin~ l.l.o.wc\qs:.slaMa..clc:ubooh.0."r(~ ~)c\qs-s 'W\\cloi~'cs. A~e Ex:Cr e\r1:\ctlc.r~u• r 's(.:.vlc)a~m.\C4bo~t" 'i'it-8~~ t.J 'I11 {, ()...... 7- ----- - -- ~--- Section4 One-way ANOVA: height(cm) versus job Source DF SS MS F p job 3 123 41 0.39 0.762 Error 36 3792 105 Total 39 3915 s = 10.26 R-Sq 3.14% R-Sq(adj) 0.00% Individual 95% Cis For Mean Based on Pooled StDev Level N Mean StDev +---------+---------+---------+--------- admin 10 175.29 12.20 (-------------*------------) clerical 10 174.57 7.17 (------------*------------) constr 10 176.54 10.27 (------------*------------) sales 10 171.76 10.74 (-------------*------------) +---------+---------+---------+--------- 165. 0 170.0 175.0 180.0 Pooled StDev 10.26 Section 5 Tabulated statistics: job, satisfaction Rows: job Columns: satisfaction 1 2 3 4 5 All admin 0 0 1 5 4 10 clerical 2 4 1 2 1 10 cons tr 0 1 2 4 3 10 sales 2 5 2 1 0 10 All 4 10 6 12 8 40 Cell Contents: Count Pearson Chi-Square 19. 800, DF 12, P-Value 0.071 Section 6 Correlations: height(cm), income Pearson correlation of height(cm) and income -0.229 P-Value = 0.154 Regression Analysis: income versus height(cm) The regression equation is income= 192952 - 715 height(cm) Predictor Coef SE Coef T p Constant 192952 86056 2.24 0.031 height(cm) -715.2 492.3 -1. 45 0.154 Descriptive Statistics: income Variable N N* Mean SE Mean StDev Minimum Ql Median Q3 Maximum income 40 0 68125 4939 31237 25206 40264 66702 92506 132806 Section 7 Descriptive Statistics: height(cm) Variable sex N N* Mean SE Mean St Dev Minimum Ql Median height (cm) f 20 0 166.78 1. 39 6.24 152.40 165.10 166.05 m 20 0 182.29 1. 42 6.37 172. 70 176.60 181. 30 Variable sex Q3 Maximum height(cm) f 168.13 179.10 m 185.40 195.00 *There is one section of this output that was not used to complete any of the problems. Write a research question that would be answered using that section of output. r--vcd0e 7"< r«i\-10'"jrd Ho .-10~ .C:S -Nfdid nof CY\d ~~< '-c' tc.e one«HM \v1q\1-\ic ~\,.'\ lvY1on3\~seY\LJobs. Pteci 'tc;\~oY\x .)(. .oo .&.19 'l .~'i5'f..'i,5 .1~S .'.)()- .CJ\r . "IS ::. 1''"' \. 0 -·'t'r0 \. ~4S'0 ~ \. 7"15"~~o \.SC>- \.'H 0 ~.oo-~.'(q 0 ~.A'iS \ ~.7&.15"7'-l5 35' "\. ~:; 7 3.~ E; ~1i"t"rcnlo e~ SQtwiple 1) ,,'lI 7'i \O(p\~' l'i~b J.\1' ''°4 ~) 7~ ns 7~ 7~ 7&, 7'f71 71 71 11 W\('CC oS~Ulf~\Scdioo/ /~lp:ti\"c:qd (Ctr\S~~ 1 I'o io\'\vsb/ci·2-sa>te -z-= '~;\oo=3.7S isS'~te-tc"\... '' i.OV\O h.it~UhC\.l\~ Ex} Suppo('·~ boxs.\Pu~ c~~~CCoto\hcwc Q.Q.ct,\'Y\~ci3n'\ 0 \,'fow\1 C~-\C\nd.atd.ov-tbcu\'\o-t.f\rd~ boxo~ P~l Cjc£oo. wc~it~\',. ntC~Qn,~U' \,\o~. ''·"4;ou~us }:cts.l 'Zsco•e is inqn ~ ''A) ·,~n.~<4 l\\~ l,.&l- \C.b to(lO-1) 0 ~b \7'°''' - I~ \'n c: 3b 77'9' 4"1 ,,,b 7l:5110 ,.., ~ "1,f80 75"0 tl ' h ,a.fOOf\V'<>t ex\Cc.lc.1 VC,4itoc-i,'iA~cW'lp.c.s• ~i >~ 7, 'S77" 7-S'o''t '7~S""l7" )..\0(a;'-l3-(7-SOJl... ').~\9"\ \O(lO-\l 7f,9>'17, 7't '"A'il 7'i.;4~1 i ISO"ll 7I "o'\°' 7q ").""\ 71)0 ~&,\:\' v~r\co .~S Cc\W--:~C~..,. o~~''~At~ec.. ~')..1&\ .-'\ssla;ti Sia.n.":cllc."\~r\c\'\(Alrs\O'Tt""edt(4\w l ~) SiQntl.atelco.l.\iCiCc.. - ------------------- ,, MAT-\~ ~.5" lot\s i·eio~e "otv.. Jh~ 76 1"- IC>S'o 'TO '' ~o "'"'~'s s l f)(F)1'the. ~ C(_vco•·e.Y-he.nout\\-e-r\\ : xrx'>Q1t l·5!~K IQR ::Q'!Ql = '60-b~ ::I~ Q, -l·'DIQR. Q 3+1.sI~R '%- \.5 (~1i) "liil·S(l~) 60 x <6'0 ~her MA't-1~ I J,C:OYc\ ExcfeQ'tDImo~\~ie\:>oJ-tr-i ..'"\~ -wh\s exentlthe m\n.mG\\IC\;.),~~ is NOTcoU'\\l~ ~ *: _ po-t Qn Q't)veQVlot\\ev'4.\ue \ 'Ul\ \l'&UtlI\U l" '°'S70 7~ ,5Cllf\C105 Probail b\ Ia. bw,~hi:mctrbeo~c.lovl c.o\Llr-ed.o~e\\C>C.AJ exwkcnis tptobabo~e\ec Qrn'dj\C~~rb\e '-\ ').'::. \ ~~!!. E") wM-{is.~lf~~ rpor-t\o\r~Md.Qrb\e.s' "t~' J..1~~1 e11w~c d1hcperun.-to\redmetrb~es .L3\ o c) I"·l './, E:W~- hthero~o bs,e\\ret''rbc1ini~tlcor~e\\ow _3.}_- orJE J1 =~o 1>_~3 _ ~6\'j "J..7. ~)wncrit lo\.o~.\e~\div\aptohc.bl\i *~EC\Ch~b1\t ibetU1eCcn\d.1 P(bh..+ P(~el +PC1wc\\1= ~ An ovtc.owit~~i~~tho\~ sl'•-l"i'j>-Cel~thaV\\ U"\CCwf1C\\owf>(O~b\\{~ Plre=.\"S\ (blue or 4e=1- . I'\S~'<5' A\eveY\~consc.\ ~u\esdt;t he \pro\> lo~sts\.5.~~ E~ l'C\s.-t°~~t~ o\4R\~' feclc\\Ukd.etevn'linrncoiti.e3rovp hClvw41chect o\ih\~omcn'.WorlCup.lsi~\bk i-h~i1sould -ihe ~robe ~&i!t.\Ho--'tht\sQ.\sH.:Qmo.Mi~pco>\'- Vcto\\ x PCx) 0 . 00\f, . 0)..150 I ~ .l'il). 3 .>~l:l.. L{ .~o'' .'tbBb MAT~l').. '3.cow\'d f.1!.. \-coC4o"\ l-\i~ >

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Ana\'\e\~bn Sptin ~'-tc.ic.t\•\'\ fxo.soc.\ \rleorhnwih~~ oletrIn\s~r~e\o e~w~eb\rl\\WiY\eo.co.clern\c. ~ch\-ev ~tvvndav.&moQ.-\.pe.enS\e.Oct'o~C\nr~co 'r\.n.ribpoons who\.\o\\()\.).J'll.~e\.\q H,: \'we.(\oO'V\~M )Gl.s.toc 'f\.ut\1\.C3\{ ;\\ lfl.Y~Y.~~~ 7-J."3-IS 11.1. l,oo~~ f:.i1~ Ho>ic\q\~oC(t'cp-lp::c\q\Yc~t:(~~.oP~=cj~~im:wo :.~'S \0, icrtq\ 9'i ~00 Low,No tJorMtl\,'l\>otfJCJ toi1P..;)~\O'f\~~ ~ =(r-1(c.-i\ p vo.\t.A~VA. F 'c..c.. \ .~dct.. 0- Ot\l..Al\OVA lC/enn\vetrli F I~l L\'l\errc.t.~...iot\ 'l"theYe~"po.rliicorce.\0••\o'W rwiIbepci&-ti~ r "'\IIrejcof"CS\~ ~A\\\e,')t-u-1\iY•~\f~ioJ\d.'c:r,,. \u,...~..al.cl1cJ~~f>P"s'°oJt~ iAl-\)_ • We studied a random sample of males and females and collected data that would allow us to address the following research interests. First, state the correct type of analysis for each problem. Then, find the answer using the output. :.,'toe'''· o.'-j\;/()' ,,,e -'IkC>··!\ I~\\¥\Yct·r.1:-,l ·l''d ,Cl, The\.:"- 'YIi..Y\ ··~nee)Jc\,~wc Gf\c eve· 4,:Ap-er-,:l-,,ot\.dl'h1'1or c-~ 11zk 1n b( 11c1nte fXYI p . ;_q'-j.G') Source DF Seq SS Adj SS Adj MS F sex 1 2405.60 2405.60 2405.60 62. 24 0.000 job 3 122.92 122.92 40.97 1. 06 0.380 Sc r-vq\vc