Multivariate analysis

Multivariate analysis is a statistical method used to study data containing multiple variables. It is used to evaluate and collect statistical data to explain and clarify relationships between the various variables associated with this data. Multivariate analysis is applied in many disciplines today including:

  • Linguistics
  • Natural sciences
  • Humanities
  • Economics
  • Insurance and financial services
  • Data mining
  • Relational databases

In these disciplines, multivariate analysis is used for:

  • Pattern recognition by providing data scientists with a better understanding of the underlying relationships and patterns in data
  • Getting comprehensive insights into data which allows better modeling and visualization of complex data
  • Predicting behavior and enhancing forecasting of probable outcomes

Common multivariate models

There are many tools and techniques used to perform a multivariate analysis such as exploratory data analysis, descriptive statistics, and quantitative regression models. In the initial stages of data analysis, most analysts will use clustering, principle component analysis, and descriptive statistics. In advanced stages of data analysis like the creation of quantitative models, analysts use regression methods like partial least squares regression. The purpose of using regression is to create a model using known responses and samples. Based on the purposeof the data analysis, multivariate analysis can be utilized to provide a better understanding of the various model outcomes. Here is a summary of the most common multivariate models:

Descriptive models

  • Principle component analysis
  • Basic statistics
  • Clustering

Regression and predictive models

  • Multiple linear regression
  • Principle component regression
  • Partial least squares regression

Classification models

  • Support vector machine
  • Linear discriminant analysis
  • Partial least squares

How you can use multivariate methods in data analysis

Multivariate analysis enables data scientists to view the behavior between variables more accurately. This enables them to identify potential issues in data, process or product. In most instances, multivariate methods are used for:

  • Obtaining an overview or a summary of a tabulated data. This type of analysis is often referred to as the factor analysis or principle components analysis. When data is summarized using multivariate methods, it is easier to identify groups, trends, outliers, and other patterns in it.
  • Analyzing groups in a table and showing how these groups differ. It can also be used to show which group each table row belongs to. This kind of analysis is often referred to as classification and discriminant analysis.
  • Identifying relationships between data columns in a table. For instance, it can be used to show the relationship between the quality of a product and the conditions of the operation process. The purpose is to use one type of variables (columns) to analyze another set and to figure out which variables are more essential in the relationship. This type of analysis is called partial least squares or multiple regression analysis, based in the size of table of data being analyzed.

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Multivariate analysis techniques

There are two major types of multivariate techniques, used to identify relationships in data, namely, dependence and interdependence. The dependence techniques try to study whether a given set of variables can predict or describe the values of other variables. The interdependence techniques refer to the inter-correlation between variables and focuses on understanding the underlying patterns and trends in data.

When choosing a multivariate analysis technique, there are a few factors you should put into consideration, the most important being the nature of variables. Variables can be categorized as metric or non-metric.

Metric variables: The metric variables are numeric in nature and always contain data that can be measured using a certain scale. Examples include profit ($3000), temperature (37° C), and age (30 years).

Non-metric data variables: The non-metric variables classifies data without specifying its magnitude. A good example would include house size (large, medium, small,), operating system (Windows, MacOS, Linux), or gender (male, female). Even if the data has an inherent order like large, medium, small, etc., it still is non-metric because the variable does not tell us how small or big the house is.

Most multivariate techniques will  perform calculations where the input data is a number. However, the technique can also work with non-metric data but the data has to be dichotomic, meaning numeric values must be assigned to it. For instance, instead of stating gender as male or female, it can be stated as 1 or 2 respectively. For more information on multivariate techniques, get in touch with our multivariate analysis assignment help experts.