Applied Regression Analysis - Midterm Exam 2 with Solutions | STAT 4230, Exams of Statistics

Download Applied Regression Analysis - Midterm Exam 2 with Solutions | STAT 4230 and more Exams Statistics in PDF only on Docsity! i t SPRING 2008, STAT 416230 SECOND MIDTERM i-, ;-\ .I |.f ,f o*n 1 rvn STUDENT lD NUMBER: Oto rz1 j8,tr {This is your 81-0-number). INSTRUCTIONS For all students: There are four Problems with a combined 1-2 parts. Write your .nrt"r, in the space provided. tf you need more space, you can use the back of a page, but clearly indicate where the continuation of your answer can be found. For all questions, show your work.'For example, don't just answer a question by'yes' or 'no' or by writing a number without any explanation. For STAT 4230 students: You must ellswer Problem 4. lt has a maximum'score of 10 points' Answer any 10 parts from the first three problems. Each has a maximum score of 9 points. (You may attempt all 1L of these parts, in which case you will receive credit for your 10 highest scores only.) For ST.AT 6230 students: Answer all 12 parts. Each part of the first three problems has a maximum score of 8 points, while Problem 4 (which consists of only one part) has a maximum score of 12 points. Good luck! Fxr* [l.rl vrs'v[< l" pROBLEM 1. A transforfiation of the dependent variable can help with a number of possible issues that we have discussed. For each of the following issues decide whether a transformation of the dependent variable tan be helpful. Your answer should be "Yes" or "No" followed.by a r.- brief explanation for your choice. , _.. K.. i To dealwith multicollinearitY. \}U. rnu\\\ica\\''atsr"t1 i5 w\'t^ -[.q'.5{r.-i., lt yvaricult p\tt n4 flre*iron a( V\F uc.\,,es \o f*err"'.,zrt u.w.&\ pe\er5 l{ wu{r afata '(t ro\kdreAr b-t $. oc+.rrc{ ca{'e\ct'cv\ b. To dealwith heterogeneitY. ,l {eS- lL*n"gcaei\ occptt ,J^<n *^-. gari-^rr- 64 {i^c lt; *c< r'\c* e'tec-\I en.''.r- vFeq^t se Cannot 6ggvc( tr,,e L lv-+ ..,--, -r^ta*.r. $.o-e'iit, a- +t4^*c{e4t\o^ oE \u< -&e(<*A*"t v4r'.alok C.^va hcfg'$ {\-{- ,r'€<,\A'a'\ \rs. (rc&\ete} rrct-e (\ot Shori Jd{trt"te'^ :et<"J' Q+..^r Ccvil l2e d?, tolCl), f;,"*.. $$er \\^e |ran$<rer'.6, tl^. vnaAo\ YrctAs \a te- Ce-e.ro*i^6! G5 it 6n1 ^.{ LrJa.tr, grcLtc*5 .rr'.t\' ncrn^el.tl oec'ct et lro€ S'r wvu{ n9 \o*.rer b{ +'.jwi{.c-^+ c. To deal with lack of normalitY tlat ',[ erkrc*t (ain\s, no {or gag'.^ w'''JA\t of lr"tx'" f* r\6tmd\\\r \oor!' a\ rs"r- nofmol fsobarril(+1 (\0" \9 €ct .\ \t^.t.." ..5 d- 9\r4\1Lt \F-(. ?&d':11 tt^'*1^ (o'o)'\{ tt^t grob(eb "t tLst {'< <nEgoi^ts &rt €.f-tr{-rs'(rr*-,., s$e*<:r) tu,a^ c 1.*5d.-^t'.cn v-bi8 \a c&- tt \L..- f,ro\otc- \: {n* tt\'<f? 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Fiftybwners of new appliances were contacted, as well as fifty owners each of 1-,2-,3- and 4-year 6ld machines. This was repeated one year later. The ten data points obtained in this way are plotted below. Prop 1.O o.9 o.8 o.7 o.6 o.5 o.4 o.3 o.2 2 age a. What common model assumption do you think might be violated with these data for ihe modelthat the manager intends to fit? Why? t{ .aff.g6rs 4\*t equ$t !ar'\c!nc{9 *..1 l're v'c\oteJ' {a eoints o'\ \kt" enA, ot'r both s''A6, &Qee4f {. !.-- s..c!t\ Urx-.ri6,1c-r< ..X'-tc lL< galv.t: In \t- tln".}Jlt (1' Z) op 'btr t(t:.^J Ovl. b. As a first step, would you recommend a transformation for the variable proportion, forthe variable age, for both, or for neither? Explain your answer, including which transformation(s) you would use, if any. t\ wc,r[ be best \o {ro^of..* {ue' Fogt'.m e€'r1 +r-4 <'.; (.fi). -G..; {,--5f,-1.,,n is $ooc\ t . Ao.LJ,t bu-"a da,{a- ( lorl.sr t..o} .rE t\- n^r\A\c) *l t. Eofr,rv'.c,. / gercen.+.4 &tr. 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