For each of the following regression models, indicate whether it is a general linear regres- sion model. If it is not, state whether it can be expressed in the form of (6.7) by a suitable transformation: a. Y; = ßo + ß₁X¿¡1 + ß2 log10 Xi2 + ß³X²₁ + ɛi b. Y; = ɛ; exp(ßo + ß₁×¡1 + ß₂X²/2) c. Y; = log10(81X;1) + B2Xi2 + εi d. Y;=ẞo exp(ẞ₁X¡1) + εi e. Y; = [1+exp(ßo + B₁X₁₁ + ε¡)]¯¯¹ General Linear Regression Model In general, the variables X₁,..., X-1 in a regression model do not need to represent different predictor variables, as we shall shortly see. We therefore define the general linear 18 Part Two Multiple Linear Regression regression model, with normal error terms, simply in terms of X variables: Y₁ = Bo+B₁Xi1 + B2X12 ++ Bp-1Xi,p-1 +εi where: Bo, B1, Xi Xi,p-1 are known constants Bp-1 are parameters &, are independent N(0, 2) i=1,...,n (6.7)

For each of the following regression models, indicate whether it is a general linear regres- sion model. If it is not, state whether it can be expressed in the form of (6.7) by a suitable transformation: a. Y; = ßo + ß₁X¿¡1 + ß2 log10 Xi2 + ß³X²₁ + ɛi b. Y; = ɛ; exp(ßo + ß₁×¡1 + ß₂X²/2) c. Y; = log10(81X;1) + B2Xi2 + εi d. Y;=ẞo exp(ẞ₁X¡1) + εi e. Y; = [1+exp(ßo + B₁X₁₁ + ε¡)]¯¯¹ General Linear Regression Model In general, the variables X₁,..., X-1 in a regression model do not need to represent different predictor variables, as we shall shortly see. We therefore define the general linear 18 Part Two Multiple Linear Regression regression model, with normal error terms, simply in terms of X variables: Y₁ = Bo+B₁Xi1 + B2X12 ++ Bp-1Xi,p-1 +εi where: Bo, B1, Xi Xi,p-1 are known constants Bp-1 are parameters &, are independent N(0, 2) i=1,...,n (6.7)

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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