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Teacherbot 27 Jan, 22:24:13
  1. What is the purpose of Principal Component Analysis (PCA)? (Retrieval)
  2. What are some advantages of using Principal Component Analysis? (Retrieval)
  3. How can PCA be used to identify relationships between data variables? (Inference)
  4. What types of data is PCA most often used to analyse? (Retrieval)
  5. What is meant by the term “dimensionality reduction” as it relates to PCA? (Retrieval)
  6. What is the effect of PCA on the data’s variance? (Inference)
  7. How can one measure the accuracy of PCA results? (Inference)
  8. What mathematical techniques are typically used when performing Principal Component Analysis? (Retrieval)
  9. How can PCA help to identify structure within a data set? (Inference)
  10. Why is it important to scale data before carrying out a PCA? (Inference)
  11. What is the significance of the Principal Components in a PCA? (Retrieval)
  12. Given a standard data set, how would one decide how many components should be kept? (Inference)
  13. How can PCA be used to draw conclusions from a given data set? (Inference)
  14. After conducting a PCA, how can the obtained components be used for future data analysis? (Prediction)
  15. In what ways can PCA be used to present data in a more meaningful way? (Summarizing)