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In this course, you will learn the theory behind dimension reduction, and get some hands-on practice using Principal Components Analysis (PCA) and Exploratory Factor Analysis (EFA) on survey data.

Requirement | Basic knowledge of operating systems (UNIX/Linux) |

Course Start | Any time, Self-paced |

Course Maker | Konstantin Tskhay |

Min Pass Mark | 70% |

All Review Questions | 50% |

Final Exam | 50% |

True/False | 1 Attempt |

Other Questions | 2 Attempt |

IBM Machine Learning – Dimensionality Reduction | Click Here |

### IBM Cognitive Class – Machine Learning – Dimensionality Reduction Answers

### Module 1: Data Series

**1. Which of the following techniques can be used to reduce the dimensions of the population?**

**2. Cluster Analysis partitions the columns of the data, whereas principal component and exploratory factor analyses partition the rows of the data. True or false?**

**3. Which of the following options are true? Select all that apply.**

### Module 2: Data Refinement

**1. Which of the following options is true?**

**2. PCA is a method to reduce your data to the fewest ‘principal components’ while maximizing the variance explained. True or false?**

**3. Which of the following techniques was NOT covered in this lesson?**

### Module 3: Exploring Data

**1. EFA is commonly used in which of the following applications? Select all that apply.**

**2. Which of the following options is an example of an Oblique Rotation?**

**3. An Orthogonal Rotation assumes that factors are correlated with each other. True or false?**

### Machine Learning – Dimensionality Reduction Final Exam Answers

**1. Why might you use cluster analysis as an analytic strategy?**

**2. Suppose you have 100,000 individuals in a dataset, and each individual varies along 60 dimensions. On average, the dimensions are correlated at r = .45. You want to group the variables together, so you decide to run principle component analysis. How many meaningful, higher-order components can you extract?**

**3. What technique should you use to identify the dimensions that hang together?**

**4. What are loadings?**

**5. When would you use PCA over EFA?**

**6. What is uniqueness?**

**7. Suppose you are looking to extract the major dimensions of a parrot’s personality. Which technique would you use?**

**8. Suppose you have 60 variables in a dataset, and you know that 2 components explain the data very well. How many components can you extract?**

**9. When would you use an orthogonal rotation?**

**10. When would you use confirmatory factor analysis?**

**11. Which of the following is NOT a rule when deciding on the number of factors?**

**12. What is one assumption of factor analysis?**

**13. What is an eigenvector?**

**14. What is a promax rotation?**

**15. What is the cut-off point for the Common Variance Explained rule?**

**16. Why would you try to reduce dimensions?**

**17. If you have 20 variables in a dataset, how many dimensions are there?**

**18. What term describes the amount of variance of each variable explained by the factor structure?**

**19. What package contains the necessary functions to perform PCA and EFA?**

**20. What is the best method for identifying the number of factors to extract?**

### Wrap Up

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