Linear Regression Model Assumptions
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For every x, Y is normally distributed with mean A+Bx and variance σ2 .
ie, E[Y|x] or μY|x =A+Bx, and v(Y|x)=σ2
Y's are independently distributed
X's are fixed quantities, or used to predict Y-values
is called the variance for the error component
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