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Welcome to this machine learning course on Dimensionality Reduction. Dimensionality Reduction is a category of unsupervised machine learning techniques used to reduce the number of features in a dataset. Dimension reduction can also be used to group similar variables together.
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.
The code used in this course is prepared for you in R
Module 1: Data Series
Question: Which of the following techniques can be used to reduce the dimensions of the population?
Question: Cluster Analysis partitions the columns of the data, whereas principal component and exploratory factor analyses partition the rows of the data. True or false?
Question: Which of the following options are true? Select all that apply.
Module 2: Data Refinement
Question: Which of the following options is true?
Question: PCA is a method to reduce your data to the fewest ‘principal components’ while maximizing the variance explained. True or false?
Question: Which of the following techniques was NOT covered in this lesson?
Module 3: Exploring Data
Question: EFA is commonly used in which of the following applications? Select all that apply.
Question: Which of the following options is an example of an Oblique Rotation?
Question: An Orthogonal Rotation assumes that factors are correlated with each other. True or false?
Question: Why might you use cluster analysis as an analytic strategy?
Question: 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?
Question: What technique should you use to identify the dimensions that hang together?
Question: What are loadings?
Question: When would you use PCA over EFA?
Question: What is uniqueness?
Question: Suppose you are looking to extract the major dimensions of a parrot’s personality. Which technique would you use?
Question: 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?
Question: When would you use an orthogonal rotation?
Question: When would you use confirmatory factor analysis?
Question: Which of the following is NOT a rule when deciding on the number of factors?
Question: What is one assumption of factor analysis?
Question: What is an eigenvector?
Question: What is a promax rotation?
Question: What is the cut-off point for the Common Variance Explained rule?
Question: Why would you try to reduce dimensions?
Question: If you have 20 variables in a dataset, how many dimensions are there?
Question: What term describes the amount of variance of each variable explained by the factor structure?
Question: What package contains the necessary functions to perform PCA and EFA?
Question: What is the best method for identifying the number of factors to extract?
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