Multivariate Analysis: Discrimination and Classification

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Questions and Answers

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 (correct)
  • To estimate the common covariance matrix
  • To predict the class of a given observation
  • To assume normality of the populations

What is the assumption about the population covariance matrices in Linear Discriminant Analysis?

  • They are linearly related
  • They are normally distributed
  • They are equal (correct)
  • They are unequal

What is the primary use of linear combinations of predictors in Linear Discriminant Analysis?

  • To predict the class of a given observation (correct)
  • To transform the data into a normal distribution
  • To identify the most important predictors
  • To estimate the population covariance matrices

What is the assumption about the predictor variables in Linear Discriminant Analysis?

<p>They are normally distributed (D)</p>
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What is the purpose of checking the univariate distributions of each variable in Linear Discriminant Analysis?

<p>To ensure that the predictors are normally distributed (B)</p>
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What is the purpose of transforming the predictor variables in Linear Discriminant Analysis?

<p>To make the data normally distributed (A)</p>
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What is the effect of not removing outliers from the data in Linear Discriminant Analysis?

<p>It increases the risk of biased estimates (C)</p>
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What is the purpose of standardizing the variables in Linear Discriminant Analysis?

<p>To make the variables' scales comparable (D)</p>
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What is the primary direction of the Linear Discriminant Analysis algorithm?

<p>To maximize the separation between classes (C)</p>
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What is the assumption about the classes in Linear Discriminant Analysis?

<p>They have identical variances (A)</p>
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