Stationarity in Time Series is one of the common assumptions in many of the time series analysis techniques. A stationary time series (or the underlying process) has mean, variance, and autocorrelation structure that do no change over time. Visually, a stationary time series will be a flat looking series, without trend, constant variance over time, a constant autocorrelation structure over time and no periodic fluctuations. In real-life scenarios lot of […]
The Linear Mixed Models procedure expands the general linear model so that the error terms and random effects are permitted to exhibit correlated and non-constant variability. The linear mixed model, therefore, provides the flexibility to model not only the mean of a response variable, but its covariance structure as well. In other words, it is an extension of the general linear model, in which factors and covariates are assumed to […]
