Discrimninant analysis frequently asked questions

Discrimninant analysis frequently asked questions

What is Discriminant Analysis ?
Discriminant Analysis in Sigma Magic is a statistical technique used to classify observations into predefined categories based on independent variables. It helps identify patterns and relationships among variables.
How do I perform Discriminant Analysis?
  • Go to the Multivariate Analysis section.
  • Select Discriminant Analysis from the available tools.
  • Input the dataset, specifying the dependent (categorical) variable and predictor (independent) variables.
  • Run the analysis to generate classification results.
    •
    What are the key outputs of Discriminant Analysis?
  • Discriminant Functions: Mathematical models used for classification.
  • Classification Table: Accuracy of classification for each group.
  • Canonical Correlations: Relationship strength between predictor variables and class labels.
  • Group Centroids: Mean values of discriminant functions for each group.
  • Mahalanobis Distance: Distance metric for classification.
    •
    What assumptions does Discriminant Analysis?
  • The predictor variables are normally distributed.
  • Homogeneity of variance-covariance matrices across groups.
  • Linear relationships between predictors and the dependent variable.
    •
    Can I visualize Discriminant Analysis results?
  • Scatter plots of discriminant functions.
  • Decision boundary plots.
  • Heatmaps for group classification.
    •
    How do I interpret the classification accuracy?
  • The number of correctly classified cases.
  • Misclassified cases for each group.
  • Overall classification accuracy percentage.
    •
    How do I improve classification accuracy?
  • Ensure predictor variables follow a normal distribution.
  • Remove redundant or highly correlated predictors.
  • Use cross-validation techniques.
  • Increase sample size to improve model generalization.
    •
    What are the common errors in Discriminant Analysis?
  • Non-normal predictor variables leading to biased results.
  • Overlapping group distributions reducing classification accuracy.
  • Insufficient sample size making the model unreliable.
  • Ignoring prior probabilities, which can affect classification outcomes.
    •
    What are the limitations of Discriminant Analysis?
  • Assumes normality of independent variables (not always true).
  • Less effective when groups have overlapping distributions.
  • Sensitive to outliers, which can distort classification results.
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    ​Reference: Some of the text in this article has been generated using AI tools such as ChatGPT and edited for content and accuracy.

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