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2009_Review1 - ECON010I Riv'uew for Midteml Elfin = Z gnu...

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Unformatted text preview: ECON010I Riv'uew for Midteml Elfin] = Z gnu-+00 3-}. Em: Exlwfln) HM +L‘r) = ELEM) + bEiY) mix) = E[x-EL><)]’" : Ett‘rffimf VELY[°'L+51) 3'- 19: VILHJ‘.) erImXH‘r) : :11 mm + b1 WHY) HOLE CaviMJ Comm) = E[(x—-Ew} (Y-EW] '3 Etxw— Etx) Ei‘f) ELLEN—.— WHXJ - vat-rm «Spa dexm :: mm) :- F.l o 3 EffiieY) Y: : Z(YI"Y) XI. ' POPULATION v. SAMPLE ' PARAMETER; v. ESTtMATakj T Rfinpm VARIABLES {39 fill {a}; E P0PULATmoM: flD+F|XI fifl‘f’filxt'i‘il A SIM PLE KEQEESJIQN ASH!“ Ffims Um); . Liam‘s in Parametarl 2 . Khhdflm japaphhg/ 3- Sample, Var-miller?» In. UNBIMED axF-hnmiory variabie limb-:43. ZLX£—?)1>o Etfllr-fi: 4. Zara Candi’c‘rwnl mm Etilxfio + 5. Hume Skadmstioflj VIM/(9.1.x): 6‘1 SLRI "5 1‘) Simrlulfy agitator-flan 0+ 5mm!“ “\3/ vmrxmmcej 0+ p: E; "[3; VM (I?!) r. 0": [W] 1 2 @— = (5.5. of Wm”) P-‘dr MULTIPLE REG RESSION Aflme‘tia'hj (MLR): I. [.1an in Parameters l-Rfl-hdom 5Lmrll‘hj/ 3.No Parfcct Cal'ihcn-Ir'rt/ L? Sumplt. VM-imiim ‘m, l Exvlwm‘tar‘y vuimblaJ L M Mutt linaw minimum”; 5. HOMDSkEDAST’I CITY MLRI- 5 => BLUE (5.2.13 T delu‘t vwiuce was“? ALL “nu-r 1 Minn-.11: («Kins-Star; like” unkind eitMndluy-s) P35 '2. A: I: #— VMMJ) T553 (I-Rfij whare, T53} : i (XU_§J:)2 .—l HI 2. R1 = R+J%MH:(J ‘frnrn mytmnr X3 am all :1th EXanhmfap-u/ A vmrikblu [her-Eh, mutant) 1 5-: : Zea : 55:2 h-rK-l h-‘k"! P—é , Cay (x; JU) Lam: TERM (+vfl w~v~€ fiz<0 NEan‘nvE BIAS PMJTWE BIAS J 2 K ' Add mm fifi-plum-fir); Var-i044“ é INCIREAJE w M; IEM‘t unchanged M 1+ wee, E mg. g. beih wubiaud WWI-51H vwfz?!) "I? Fara-f“ E? If (31$!) [3: “used I {I}. LILHLIMEJ “ff/I) «1' WW (f?!) ~ TRADE OFF betwabn vmriMxLe .3 mnbmxed r1251 [’10 TO IN ! ERPRET COEFFICIENTS OF DIFFERENT W e) Lexie-lame} Medal WMedel: y=fia+fiixa=a E£yle)¥fin+fi,x fin=5(fi’lx=0) =dEle) 1%- d1, Far 1 unit increase in 1:, expected y changes by 31. b) Log-level Model True Model: hay = fie + 5135+ 5 E{1ny|x)= 5n+511 5u=EUHyIX=CO fl = dEUnylx) = dE(ylx)fE(y 1x) = %ay+100 E (it ——-=I—_d¥ —-—M =2} My = 100cm Far 1 unit increase in x, expected y changes by 100 I3. %. fi)L-WWMDEIBI MW y=fiu+fixlnx+a E(_y.|lanx)ifiu +511nx fibéflyllnwfl) dEUIInx) _ fly Ay x100 El 3 dlnx _ %Ax+100 -%Ax fil _ £1}! =-='—— -9’Ax fl Far 1 % increase in x, expected 3' changes by W‘ d) Leg-10g Model True Model: lntyaé?fl + [.33 lnx+£ E(lny|lnx} = ,fi'flflfi‘I In: fig =E(lnyliflx:0) =W= dE£y|x)fE(y]x) = %Ay+100 _ %3y ’3‘ Jim: awx Voflx+100 _ 935:: For 1 "A: increase in x, expected y changes by 5. %. Witm WW5:@¢—h Wis om Std. Error t-Stafiltk: Prob. 4"; {if 41155515 55.54155 1.555551 5.1222 it a @ p, 4 3.353554 5.555355 5.527253 5.5555 =' .:- 4:751 Mean dependent 1war 95?- 9455‘‘ 14. 245253 -._-" . . . 1435534 1.45 55551555 5551.422 55151554 55.54555 mwmn stat 1.552114 5515(5- -5tati5tic} 5.555555 Some key words in answm‘ g questions: Simple regression: > Expected! on average! predicted Multiple regression: 3* Expected! on average! predicted F Holding other factors constant —thJ (Y) ...
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