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Relational statistics fall into one of three categories: univariate, bivariate, and multivariate analysis. Univariate analysis is the study of one variable for a sub-population. Bivariate analysis is the study of a relationship between two variables. Multivariate analysis is the study of relationship between three or more variables. The relational statistics include correlation, regression, discriminant analysis, conjoint analysis, factor analysis and cluster analysis
• Discriminant analysis: It is used to classify people or objects into groups based on several predictor variables. The groups are defined by a categorical variable with two or more values, whereas the predictors are metric. The effectiveness of the discriminant equation is based not only on its statistical significance but also on its success in correctly classifying cases to groups.
• Conjoint analysis: It is a technique that typically handles non-metric independent variables. It allows the researcher to determine the importance of product or service attributes and the levels or features that are most desirable. Respondents provide preference data by ranking or rating cards that describe products. These data become utility weights of product characteristics by means of optimal scaling and log linear algorithms.
• Factor analysis: It attempts to reduce the umber of variables and discover the underlying constructs that explain the variance. A correlation matrix is used to derive a factor matrix from which the best linear combination of variables may be extracted.
• Cluster analysis: It is a set of techniques for grouping similar objects or people. The cluster procedure starts with an undifferentiated group of people, events or objects and attempts to reorganize them into homogeneous sub-groups.
Titany answered the question on October 21, 2021 at 13:50
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