An alternative to the regression formula y = + x expresses each y value, rather than

An alternative to the regression formula μy = α + βx expresses each y value, rather than the mean of the y values, in terms of x. This approach models an observation on y as
y = mean + error = α + βx + ɛ,
Where the mean μy = α + βx and the error = ɛ.
The error term denoted by ɛ (the Greek letter epsilon) represents the deviation of the observation from the mean, that is, ɛ = error = y - mean.
a. If an observation has ɛ > 0, explain why the observation falls above the mean.
b. What does ɛ equal when the observation falls exactly at the mean? The ɛ term represents the error that results from using the mean value (α + βx) of y at a certain value of x for the prediction of the individual observation on y.
c. For the sample data and their prediction equation  = α + βx, explain why an analogous equation to the population equation y = α + βx + ɛ, is y = a + bx + e, where e is the residual, e = y - . (The residual e estimates ɛ. We can interpret e as a sample residual and ɛ as a population residual.)
d. Explain why it does not make sense to use the simpler model, y = α + βx, which does not have the error term.