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Discriminant Analysis

This presentation guide you through Discriminant Analysis, When To Use Discriminant Analysis?, Descriptive discriminant analysis, Predictive discriminant analysis, Researchers overcome Type I error, Benefits of Discriminant Analysis and Discriminant Analysis Procedure.<br><br>For more topics stay tuned with Learnbay.

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Discriminant Analysis

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  1. Discriminant Analysis Swipe

  2. Discriminant Analysis Discriminant analysis is a versatile statistical method often used by market researchers to classify observations into two or more groups or categories. In other words, discriminant analysis is used to assign objects to one group among a number of known groups. In order to perform any kind of discriminant analysis, you must first have a sample within these known groups.

  3. When To Use Discriminant Analysis? By performing discriminant analysis, researchers are able to address classification problems in which two or more groups, clusters, or populations are known up front, and one or more new observations are placed into one of the known classifications based on measured characteristics. Discriminant analysis is also used to investigate how variables contribute to group separation, and to what degree. For this reason, it’s often leveraged to compliment the findings of cluster analysis.

  4. Market researchers are continuously faced with situations in which their goal is to obtain a better understanding of how groups (customers, age cohorts, etc.) or items (brands, ideas, etc.), differ in terms of a set of explanatory or independent variables. These situations are where discriminant analysis serves as a powerful research and analysis tool.

  5. Descriptive discriminant analysis Descriptive discriminant analysis is used when researchers want to assess the adequacy of classification, given the group memberships of the object under study.

  6. Predictive discriminant analysis Predictive discriminant analysis is used when researchers want to assign objects to one of a number of known groups of objects.

  7. Researchers overcome Type I error. In discriminant analysis, the intercorrelation of variables is addressed by partitioning correlations between independent variables. When discriminant analysis uses one independent variable to rationalize differences between the groups, the remaining variables are amended so that any difference that is apparent between groups is not due to correlation that the other independent variables have with the first variable. For this reason, discriminant analysis only addresses the unduplicated variance between groups.

  8. Benefits of Discriminant Analysis Discriminant analysis can be closely compared to regression analysis for the ways in which it identifies the degree to which objects adhere to the specifications of certain groups. Discriminant analysis is also commonly used by marketers to develop perceptual maps.

  9. There are seemingly endless ways to implement discriminant analysis for market research and business purposes. By conducting this method of data analysis, researchers are able to obtain a much stronger grasp on the products and services they provide, and how these offerings stack up against varying topics and areas of interest.

  10. Discriminant Analysis Procedure Step 1: Collect training data Step 2: Prior Probabilities Step 3: Bartlett's test Step 4: Estimate the parameters of the conditional probability density functions Step 5: Compute discriminant functions Step 6: Use cross validation to estimate misclassification probabilities Step 7: Classify observations with unknown group memberships

  11. Topics for next Post Factor Analysis Linear Regression Association Rule Stay Tuned with

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