![correlation matrix eviews correlation matrix eviews](https://www.statology.org/wp-content/uploads/2020/01/correlationMatrix0.jpg)
The steps are reproduced in Figures 1a and 1b.įrom here, we can derive the usual summary statistics by clicking on View in the group window, moving the mouse over Descriptive Stats and clicking on Common Sample. This will open a group object in a spreadsheet with the four variables placed in columns. We can do this by selecting all four variables in the workfile by clicking on each while holding down the Ctrl button, right-clicking on any of the highlighted variables, moving the mouse pointer over Open in the context menu, and finally clicking on as Group. To understand our data, we will first create a group object with the variables of interest. The data contains four variables, three of which pertain to arrests associated with (and naturally named) MURDER, ASSAULT, and RAPE, whereas the last, named URBANPOP, contains the percentage of the population living in urban centers. In particular, our dataset summarizes the number of arrests per 100,000 residents in each of the 50 US states in 1973. Principal Component Analysis of US Crime Data The links to the workfile and program file can be found at the end.
![correlation matrix eviews correlation matrix eviews](https://i.ytimg.com/vi/wfGzcXEgcPc/hqdefault.jpg)
Determine how much variation each variable contributes in each principal direction.Quantify how much variation (information) is explained by each principal direction.Extract all principal (important) directions (features).Summarize and describe the dataset under consideration.In particular, we are motivated by a desire to apply PCA to some dataset in order to identify its most important features and draw any inferential conclusions that may exist. Here, we aim to complement our theoretical exposition with a step-by-step practical implementation using EViews.
CORRELATION MATRIX EVIEWS SERIES
In Part I of our series on Principal Component Analysis (PCA), we covered a theoretical overview of fundamental concepts and disucssed several inferential procedures.