Multivariate Statistical Analysis : Natural And Physical Processes

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Multivariate Statistical Analysis
Statistical methodology designed to obtain information from data sets that include simultaneous measurements on many variables is called multivariate statistical analysis (1). Multivariate statistics help to study how the variables are related to one another, and also how they work in combination to differentiate between the cases on which the observations are made. Several research disciplines such as biology, medicine, environmental Science, Psychology, Sociology, Economics, Education, Archaeology, Anthropology have broad applications of Multivariate statistical analysis (2).
Numerous interesting research questions about natural and physical processes are so complex that they require multivariate models and multivariate statistics. There are several types of multivariate models such as Multidimensional Scaling, Principal Components Analysis, Cluster Analysis, Linear Mixed-Effects Modeling, Canonical Correlation, Multivariate Factor Analysis, Logistic regression and so on, each with its own type of analysis (3).
Logistic Regression
Logistic regression is also called Logit regression or Logit mode. It is developed by David Cox in 1958. Logistic regression is an analogous method for the multiple linear regression. Unlike multiple linear regression, here the outcome or response variable (categorical) is all or none that is the dependent variable is dichotomous or binary. Here the response is defined as an indicator (dummy)
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