Tutorial 61. For each of the following regression models, indicate whether it is a general linearregression model. If not, state whether it can be expressed in the form of a linearregression model after some suitable transformationa.Yi=β0+β1Xi1+β2logXi2+β3X2i1+εib.Yi=εiexp(β0+β1Xi1+β2X2i2),withεi>0c.Yi=β0log(β1Xi1)+εid.Yi=log(β1Xi1β2logXi2+εie.Yi=[1+exp(β0+β1Xi1+εi]-12. Consider the multiple linear regression modelsYi=β1Xi1+β2Xi2+εi,i=1, ..., nwhereεiare uncorrelated withEεi=0and2i=σ2state the least square criterionand derive the least squares estimators forβ1andβ2.3. Consider the multiple regression modelsYi=β0+β1Xi1+β2X2i1+β3Xi2+εi, ..., nwhereεiare uncorrelated withi2i=σ2. state the least square criterionand derive the least squares normal equations.4. An analyst wanted to ±t the regression modelYi=β0+β1Xi1+β2Xi2+β3Xi3+εi, ..., nby the least squares estimation when it is known that
This is the end of the preview. Sign up
access the rest of the document.
This note was uploaded on 10/04/2010 for the course STAT ST3131 taught by Professor Xiayingcun during the Fall '09 term at National University of Singapore.