Multivariate analysis
Multivariate analysis is a branch of statistics that examines multiple variables to understand the effect of each on the response variable. It is used when multiple measurements are made on each observation and when the variables are interconnected.
Overview[edit | edit source]
Multivariate analysis can be complex as it involves the simultaneous observation and analysis of more than one statistical outcome variable. The technique is used in social science, psychology, biostatistics, machine learning, geology, and marketing among other fields.
Types of Multivariate Analysis[edit | edit source]
There are several types of multivariate analysis, and the correct choice depends on the type of data and the specific objectives of the analysis. Some of the most common types include:
- Factor Analysis: This is used to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables.
- Cluster Analysis: This technique is used to classify objects or cases into relative groups called clusters.
- Discriminant Analysis: This is used to determine which variables discriminate between two or more naturally occurring groups.
- Multiple Regression Analysis: This is used to predict the value of one variable based on the values of two or more other variables.
- Canonical Correlation Analysis: This is used to identify and measure the associations among two sets of variables.
Applications[edit | edit source]
Multivariate analysis is used in many fields, including:
- Social Science: In social science, multivariate analysis is used to examine patterns of response to multiple variables.
- Psychology': In psychology, it is used to understand the relationship between different psychological variables.
- Biostatistics: In biostatistics, it is used to analyze the effect of multiple variables on patient outcomes.
- Machine Learning: In machine learning, it is used to understand the relationship between different features of a dataset.
- Marketing: In marketing, it is used to understand the relationship between different aspects of consumer behavior.
See Also[edit | edit source]
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