search for: inp8

fit1<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc) fit2<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.5,data=wbc) fit3<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc) fit4<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc) fit5<-rq(for...

quantreg package is used. *fit1 results are* Call: rq(formula = op ~ inp1 + inp2 + inp3 + inp4 + inp5 + inp6 + inp7 + inp8 + inp9, tau = 0.15, data = wbc) Coefficients: (Intercept) inp1 inp2 inp3 inp4 inp5 -0.191528450 0.005276347 0.021414032 0.016034803 0.007510343 0.005276347 inp6 inp7 inp8 inp9 0.058708544 0.005224906 0.006804871 -0...

...nterpret the above beta coefficients and what do they mean exactly??. t=0.5 means are we considering first 50% of the total data? t=0.6 means are we considering the first 60% of the total data? can we write a equation like y=intercept+b1*inp11+b2*inp29+b3*inp3+b4*inp4+b5*inp5+b6*inp6+b7*inp7+b8*inp8+b9*inp9 as in Linear Regression to calculate the predicted output of y or not?? If we are taking into consideration 5 quantiles of data ,Does it mean that we are dividing data it into 5 parts??which variables i have to consider if the data is to be divided into 5 parts? And i got 5 equatio...