Q: Conditions of Mixed, Random, fixed models Under what conditions is a design said to be :
1. Fixed effects model
2. Mixed effects models.
Or can anyone suggest a suitable pdf of the above content?

A: A linear model is said to be fixed if it contains only fixed variables, usually denoted $\beta$. Moreover, any randomness not accounted for in these variables is explained in the error term $\epsilon$. A mixed effect model contains random effects, sometimes denoted $S$. These help extract some of the "systematic" randomness from $\epsilon$.
Both fixed and random effects are a part of the linear model and may act as coefficients on the data variable $X_i$.
There're a lot of attempts at explaining this, I have yet to find the "best." Consider (Barr, Levy, Scheepers, & Tily, 2013) for one potential treatment.
