Why Do Multivariate Analysis?

In this section, I'm going to provide you with a brief introduction to something known as multivariate analysis. And this is about this refers to a body of statistical techniques that have been widely used to analyze data both in environmental and other numerate fields. And it refers to a suite of statistical techniques that are used to analyze data comprising of more than one variable in many disciplines disciplines such as community ecology. We have several samples and we wish to explore the relationship between samples in terms of species composition or any other response variable. And it is the presence of multiple species or multiple groups within a response variable that makes our data multivariate. Ordination is an important aspect of MVA and different ordination methods take samples, sites and reorder them according to species composition. It is also possible to use predictor variables, say environmental conditions, to categorize the data. There are two main types of MBAs that we essentially use. This includes things like ingredient, indirect gradient analysis, and we just start with the species or any other response variable in the various samples that you may have collected across different sites or from different areas and suchlike. The impact of predictors is inferred later on, and indirect gradient analysis includes methods like the principal component analysis, correspondence, analysis and nmds. We look for patterns in data and their plausible causes by examining patterns of species composition or any other response variable across different sites. Direct gradient analysis uses both the response variable and the predictors to identify the patterns in the data. Example things like cluster analysis or unsupervised classification, which is carried out on the basis of. Using a response variable and the predictors that define the different groupings in the response variable. And then we can cluster our response, say different species on the basis of predictors, say things like sepal length and petal length. All these methods use some form of dissimilarity, matrix or distance measure to separate out the different species or different response groups. In this section we are just going to touch upon some of the most widely used multivariate analysis techniques things such as cluster analysis, principal component analysis, correspondence analysis, to name a few, and all those lectures. Are coming up further on in this section, and by the time we are done, you will be. Able to implement many of these techniques practically and implement and interpret the results.