10 Questions
What is the main objective of transforming multivariate observations to univariate observations in Linear Discriminant Analysis?
To separate the new observations derived from population 1 and 2 as much as possible
What is the assumption about the population covariance matrices in Linear Discriminant Analysis?
They are equal
What is the primary use of linear combinations of predictors in Linear Discriminant Analysis?
To predict the class of a given observation
What is the assumption about the predictor variables in Linear Discriminant Analysis?
They are normally distributed
What is the purpose of checking the univariate distributions of each variable in Linear Discriminant Analysis?
To ensure that the predictors are normally distributed
What is the purpose of transforming the predictor variables in Linear Discriminant Analysis?
To make the data normally distributed
What is the effect of not removing outliers from the data in Linear Discriminant Analysis?
It increases the risk of biased estimates
What is the purpose of standardizing the variables in Linear Discriminant Analysis?
To make the variables' scales comparable
What is the primary direction of the Linear Discriminant Analysis algorithm?
To maximize the separation between classes
What is the assumption about the classes in Linear Discriminant Analysis?
They have identical variances
This quiz covers discrimination and classification in multivariate analysis, originally developed by R.A. Fisher in 1936. It explores predicting the probability of belonging to a given class and the use of discriminant analysis with continuous and/or categorical predictor variables.
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