Swot Analysis

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2 A brief overview of the classical linear regression model Learning Outcomes In this chapt, er ou wil learn who ot y ● eDriv the OLS formulae for timating paraersmt and their es andadr eros ts ● pani the aelb ppresit tat a dog arot udl xE l rised or h mit se ohs haev ● us the fasrotc tat afftce the zse of aadr erso csiD h is ts dn ● tes T ypothes using the tes of sign h cance and con denc al appacseh vre tni or ● pter re tnI p ause vl ● aet gernois sledom adn tes elgnis ptoseh ni mit sE yh sw eiVE 2.1 What is a regression model? Regnoisr aasiyl si atsoml recaylni teh tsom proatn lot at the n t im t as n pa. ut at si gernois aa?siyl nI ervy al ic rtemon sid so l B hw n ren g er,smt gernois si recnod tiwh gnibrcsed adn auagnit the ve l relationship between a given variable and one or more other variables. Moer spice a c gernois si an apt ot pani stnemvo ni a aaelb yb fecnr me t xe l v ir er ot stnemvo ni eno ro erom otreh aa. v ir selb o T ake tsih erom ,etrcno etond teh aaelb esohw stnemvo m v ir teh gernois ks ot pani yb es xe l y adn the aaselb cihw aer udes ot v ir pani tesoh aasnoit yb x1 , x2 , . . . ,ecn ni tsih aylevit pel xe l v ir , xk H ler mis up, ti udl eb adi tat aasnoit ni aselb t(eh xs) cause chaesng tes ow s h v ir k air v ni emos toreh aa,elb v ir y. sihT capret liw eb detiml ot the aes erhw h c teh ledom ks ot pani casegn ni ylno eno aaelb es xe l h v ir y (although this noitcrse liw eb edvomr in captre ). h 6 27 28 Introductory Econometrics for Finance Box 2.1 Names for y and xs in regression models Names for y Dependent variable Regressand Effect variable Explained variable Names for the xs Independent variables Regressors Causal variables Explanatory variables erhT are aus pylet creaaelb asem for v oir moc h tni e gn n xs, and all of thes ermst wil be used ymouysl in

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