Why Do Multivariate Analysis?

Minerva Singh
A free video tutorial from Minerva Singh
Bestselling Udemy Instructor & Data Scientist(Cambridge Uni)
4.3 instructor rating • 39 courses • 70,078 students

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Applied Statistical Modeling for Data Analysis in R

Your Complete Guide to Statistical Data Analysis and Visualization For Practical Applications in R

09:35:11 of on-demand video • Updated August 2020

  • Analyze their own data by applying appropriate statistical techniques
  • Interpret the results of their statistical analysis
  • Identify which statistical techniques are best suited to their data and questions
  • Have a strong foundation in fundamental statistical concepts
  • Implement different statistical analysis in R and interpret the results
  • Build intuitive data visualizations
  • Carry out formalized hypothesis testing
  • Implement linear modelling techniques such multiple regressions and GLMs
  • Implement advanced regression analysis and multivariate analysis
English [Auto] In this section I'm going to provide you with a brief introduction to something known as multivariate analyses 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 barely able 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 has a presence of multiple species or multiple groups within a response variable that makes a for data multi-variate ordination of an important aspect of M.V. and different ordination methods. Big 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 M.V. is that we essentially use this includes things like ingredient in garlic gradient analyses and we just start with a species or any other response variable in the various samples that you may have collected across different sites or from different areas and such like the impact of predictors is inferred later on. And in that gradient analyses includes matters like the principle component analyses corespondents analyses and D.S. we look for patterns in data and there are plausible causes by examining patterns of species composition or any other response variable across different sites direct gradient analyses uses both the response variable and the predictors to identify the patterns in the data. Example things like cluster analyses or unsupervised classification bitches get it out on the basis of using a response variable and the predictors that define the different groupings and the response variable and then we can cluster of a response say different species on the basis of predictors say things like sepulture and battlement. All these methods use some form of dissimilarity matrix or distance measure to separate out different species or different response groups. In this section we are just going to touch upon some of the most widely used multivariate analyses techniques things such as cluster analyses principal component analyses correspondence analyses 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.