In this chapter, we will work through a number of examples of analysis that are inspired in part by a few of the problems introduced in “Spectral Analysis for Neural Signals.” Our purpose here is to introduce and demonstrate ways to apply the Chronux toolbox to these problems. The methods presented here exemplify both univariate analysis (techniques restricted to signals elaborated over a single time course) and bivariate analysis, in which the goal is to investigate relationships between two time series. Problems involving more than two time series, or a time series combined with functions of spatial coordinates, are problems for multivariate analysis. The chapters “Multivariate Neural Data Sets: Image Time Series, Allen Brain Atlas” and “Optical Imaging Analysis for Neural Signal Processing: A Tutorial” deal explicitly with these techniques and the use of the Chronux toolbox to solve these problems.