ABSTRACT
This chapter introduces the basic materials and data analytic tools of statistical and probabilistic analysis. More complicated patterns in the data may be investigated by use of correlation matrices, principal component decompositions, cluster analysis, or conditional plots where levels of a third variable are held constant. The chapter reviews standard graphical methods to investigate the properties of data. When seeking to model real-world phenomena, especially in biological or ecological settings where nonlinearity and the rescaling of measurements are common, visualization and graphical representation are important tools. These include scatterplots, boxplots, histograms and multivariate versions of these when many variables are to be examined. The chapter examines a cross-tabulated table of counts and related frequencies. Most model building is focused on the detection and modeling of relationships between responses of interest variables and sets of explanatory variables. A useful plot is the simple scatterplot, plotting the values of one variable versus another.
