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Description The second part of a two-part work on multivariate analysis is concerned with the more specialized techniques that follow on from the basic theory presented in part one. The growing data increased the demands for Data Scientist and Data Analyst and hence this might be the reason that you are reading this article :. Now a question you might ask : But what does Data Analysis has to do with all these AI driven fields???
Look it this way, we have a lot of data available to be used, most of it in reality is junk and not efficient to be used directly in a machine learning algorithm. In this article I will be discussing the Multivariate Analysis and the basic concepts constituting it.
Multivariate techniques are popular because they help organizations to turn data into knowledge and thereby improve their decision making. Most of the Multivariate analysis techniques are extensions of univariate analysis of single variable and bivariate analysis techniques used to analyze two variables.
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Other multivariate techniques are solely created to deal with the multivariate problems, such as discriminant analysis or factor analysis etc. To be considered truly multivariate all the variables must be random and interrelated in such a way that their different effects can not meaningfully be interpreted separately. The Basic concepts of Multivariate Analysis There are some key concepts that constitutes the Multivariate analysis :.
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The Variate The building block of the multivariate analysis is the variate. It is defined as the weighted sum of the variables, where the weights are defined by the multivariate techniques. The variate of n weighted variables X1 to Xn can be written as :. But what actually are these variates used for?
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These variates captures the multivariate features of the of the analysis, thus in each technique, the variate acts as the focal point of the analysis. For example, in multiple regression, the variate is determined in such a manner that the correlation between the dependent variable and the independent variables is maximum.
Measurement Scales Multivariate analysis involves dealing with data which has multiple variables and so the entries in these variables might have different scales. Measurement is important in accurately representing the concept of interest and is instrumental in the selection of the appropriate multivariate technique. Data can be classified into 2 categories : non-metric qualitative metric quantitative Determination of each variable as non-metric or metric is essential as it can change the whole analysis.
This identification is done by humans as to computers everything is just numbers. Non-Metric Measurement Scales Non-metric data describes differences in type or kind by indicating presence or absence of a characteristic or property.
These properties are discrete, such that it signifies only the presence of a particular feature and absence of all other features. For example : gender.
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Two or more rows of multivariate continuous data. The columns should be first all variates of first block, then all variates of second block. Departures from multivariate normality detectable as departure from multivariate skewness or kurtosis.
Two multivariate samples of measured data, or two square variance-covariance matrices, marked with different colors. Testing for equality of the means of several multivariate samples, and ordination based on maximal separation multigroup discriminant analysis. Two or more samples of multivariate measured data, marked with different colors.
http://mx13.profession70476.tokyo/12266.php The number of cases must exceed the number of variables. Two or more groups of multivariate data, marked with different colors, or a symmetric similarity or distance matrix with similar groups. Testing for difference between multivariate groups, based on any distance measure. The groups are organized into two factors of at least two levels each.
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