Sentence examples for moving average analyses from inspiring English sources

To visualise the different patterns of learning across trials for each task and stimulus pair type, "five-point moving average" analyses were employed to construct learning curves consisting of 116 overlapping windows moving in one trial steps, which were averaged across participants.

This state-space model-based analysis has a number of advantages over moving average or change-point analyses, including the ability to estimate confidence bounds for individual subjects and greater sensitivity to changes associated with learning [56].

In time series analyses, autoregressive moving average model and its variants (ARMA, ARMAX and ARIMA) are widely used especially in electronic engineering and system identification fields, and some unit root tests (for stationarity test in time series including Augmented Dickey Fuller test [ 22]) have been used in statistics and econometrics as well.

In transportation analyses, autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models have been widely used mainly because of their well established theoretical foundation and ease of application.

For this reason we have taken a conservative approach and report results from analyses using a moving average interval of 150 days.

Trends were analysed using autoregressive moving average time series and mixed effects linear regression models adjusting for all available potential confounders.

Fourth, mixed autoregressive and moving average model [ARMA (1,1)] regression analyses were conducted to evaluate time trends in the silica exposure levels.

We conducted regression analyses for the 2-day moving average stratified by study site and controlled for covariates used in Model 1 to check heterogeneity of site-specific associations.

The first one was city-specific time series analyses by means of autoregressive integrated moving average (ARIMA) modelling [34].

To verify the robustness of our findings at the suggestion of one of the reviewers, we subsequently repeated analyses using the Box Jenkins autoregressive integrated moving average (ARIMA) approach.

ORs and 95% CIs per SD increase in pollution concentration from the primary GEE unconditional logistic regression analyses for apnea and bradycardia using a moving average of pollution lagged 0 and 1 day are presented in Table 4.