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Extra info for A Genetic Algorithm Tutorial [jnl article]

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The flat dashed lines in baseline and treatment phases in this figure show the mean level of the dependent variable. Also shown in this figure is the difference between the phase means, or the change in mean from baseline to treatment phase. The change in means between phases is a second form (and special case) of change in level between phases. Immediacy of Change Another pattern that can be used to assess the change from a baseline to a treatment phase, or between two different but adjacent treatment phases, is the latency of change (Kazdin, 1982).

This condition is termed the bounds of invertibility. Conceptually, a moving average model says that preceding random shocks influence current observations, and the moving average term tells by how much (McCain & McCleary, 1979). An ARIMA(0,0,2) model can be expressed as, Y t ¼ st À M 1 st À 1 À M 2 st À 2 ; ð2:6Þ with the bounds of invertibility, M1 þ M2 < 1; M1 À M1 < 1; and jM2 j < 1: If these conditions do not hold, the series is nonstationary. Conceptually, a second-order moving average model says that the two preceding random shocks influence the current observation, Yt, and the moving average terms tell us by how much (McCain & McCleary, 1979).

Notice the significant positive lag-1 autocorrelation, followed by an abrupt cutoff to randomly dispersed, statistically nonsignificant partial autocorrelations, the signature of white noise. 6 show illustrative ACFs and PACFs suggestive of an AR(1) process in which the autoregressive parameter A1 is negative. 5 Autocorrelation function (ACF) of an ARIMA(1,0,0) process where A1 is negative. 6 Partial autocorrelation function (PACF) of an ARIMA(1,0,0) process where A1 is negative. exponentially decaying manner.

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