The analysis of experimental data that have been observed at different points in time leads to unique problems in statistical modeling and inference. The obvious correlation introduced by the sampling of adjacent points in time can severely restrict the applicability of the many conventional statistical methods traditionally dependent on the assumption that observations are independent and identically distributed (or a random sample). The systematic approach by which one goes about answering the mathematical and statistical questions posed by dependent data is commonly referred to as time series analysis.