### Principal Components - Example

##### March 28, 2016 | Uncategorized | No Comments

In the following example, a principal components analysis was performed on the Chatterjee-Price attitude dataset, which contains the aggregated answers to questionnaires of 35 employees from 30 randomly selected departments of a large financial firm. The variables measure employee perceptions of managerial aspects of their employment experience and reflect favorable ratings. These managerial aspects and […]

Read More### Principal Components – Theory

##### March 28, 2016 | Uncategorized | No Comments

In principal components analysis (PCA), original variables within a sample are transformed as linear combinations into a new set of variables that is uncorrelated. PCA can act as an exploratory technique, allowing underlying factors to be identified for factor analysis. PCA can also be used to make data manipulation easier. The new set of variables, […]

Read More### Logistic Regression

##### March 25, 2016 | Uncategorized | No Comments

Like ordinary least squares regression, logistic regression employs one or more independent variables to predict a dependent variable. Unlike OLS regression, the dependent variable is not continuous but is instead a categorical variable that takes on the values of 0 and 1. Like OLS, fitting a logistic regression produces coefficients of independent variables whose values […]

Read More### Factor Analysis

##### March 25, 2016 | Uncategorized | No Comments

Factor analysis is similar to principal components analysis (PCA) in that it attempts to identify latent variables that lie behind the data. However, where PCA forms linear combinations of the data’s variables, factor analysis states the observed variables as linear combinations of latent, or underlying, variables. Thus, its purpose is to identify the presence of […]

Read More### Cluster Analysis

##### March 25, 2016 | Uncategorized | No Comments

Clustering is an exploratory technique in which a researcher attempts to identify clusters within a group of data. Several techniques exist to determine clusters, the best known of which are hierarchical and K-means clustering. Within hierarchical clustering are agglomerative and divisive methods. The former method begins with each of the N observations forming its own […]

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