**A walk through Discriminant analysis**

Today, the class started with the introduction of Discriminant analysis. Discriminant analysis is an *a priori* technique. That is, you have the groups defined before you begin. Multiple Discriminant analysis, from which Discriminant maps are drawn, is a case where you have membership from more than one group. Discriminant analysis is used in situations where you want to build a predictive model of group membership based on observed data - characteristics, attitudes, demographic attributes, etc. The analysis produces a linear equation of variables that can be used to explain which attribute best discriminates between the two groups and, as an extension, build a powerful predictive model for future classification. Sometimes clients confuse Discriminant analysis with cluster analysis. In fact, they are conceptually similar. However, one uses cluster analysis to form groups. Discriminant flows in the opposite direction: You have the groups, you want to know why? Discriminate analysis, a multivariate technique used for market segmentation and predicting group membership is often used for this type of problem because of its ability to classify individuals or experimental units into two or more uniquely defined populations. Choosing which predictor variables will be included in the analysis requires a bit of marketing sense. For example, the client may seek to distinguish between high-probability customers and low-probability customers. Taking a survey into consideration the respondents may asked to rate the company on a given array of attributes - rankings of importance, performance, company image, and firm demographics such as size, revenue, number of employees, and geographic area. A good analysis, especially if it is going to be used for back classification, cannot use all the data available. The results would be murky and there would be a good deal of variation error, commonly referred to as noise. Therefore, it is equally vital to choose which predictors go into the equation.

Group – Marketing 3

Author of the Article-Rahul Ghosh(13157)

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