line">\(Q\)</span>, <span class="math inline">\(\mbox{MA}(Q)\)</span>,
there is an overall mean parameter <span class="math inline">\(\mu\)</span> and regression coefficients
<span class="math inline">\(\theta_q\)</span> for previous error terms. With <span class="math inline">\(\epsilon_t\)</span> being the
noise at time <span class="math inline">\(t\)</span>, the model for outcome <span class="math inline">\(y_t\)</span> is defined by
<span class="math display">\[
y_t = \mu + \theta_1 \epsilon_{t-1} + \cd