An experiment in randomized complete block design with four replications was conducted to compare the average daily gain in weight (ADG) of piglets given four doses of feed additive. However, the initial weights of piglets differed. Hence, the researchers conducted an Analysis of Covariance(ANACOVA) to adjust the final weights of piglets. 14. Which of the following is(are) necessary condition(s) for a valid ANACOVA?1. The ADG of piglets must be linearly dependent on the initial weights of piglets.II. The initial weight of piglets must be independent on the final weights of piglets.D. Neither I nor IIA. I onlyB. II onlyC. Both I and II15. Which of the following is(are) the appropriate linear model(s) for the ANACOVA?1. Yij = μ + Ti +pj + B(Xij − X) + EijII. Y¡j = µ + Ti + B(Xij − X) + EijA. I onlyB. II onlyC. Both I and IID. Neither I nor II

The randomized complete block design is applied was conducted The study is on comparing the average…

Answered: An experiment in randomized complete…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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An experiment in randomized complete block design with four replications was conducted to compare the average daily gain in weight (ADG) of piglets given four doses of feed additive. However, the initial weights of piglets differed. Hence, the researchers conducted an Analysis of Covariance (ANACOVA) to adjust the final weights of piglets.

14. Which of the following is(are) necessary condition(s) for a valid ANACOVA?
1. The ADG of piglets must be linearly dependent on the initial weights of piglets.
II. The initial weight of piglets must be independent on the final weights of piglets.
D. Neither I nor II
A. I only
B. II only
C. Both I and II
15. Which of the following is(are) the appropriate linear model(s) for the ANACOVA?
1. Yij = μ + Ti +pj + B(Xij − X) + Eij
II. Y¡j = µ + Ti + B(Xij − X) + Eij
A. I only
B. II only
C. Both I and II
D. Neither I nor II

Definition Definition Measure of how two random variables change together. Covariance indicates the joint variability or the directional relationship between two variables. When two variables change in the same direction (i.e., if they either increase or decrease together), they have a positive covariance. When the change is in opposite directions (i.e., if one increases and the other decreases), the two variables have a a negative covariance.

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