Best writers. Best papers. Let professionals take care of your academic papers

Order a similar paper and get 15% discount on your first order with us
Use the following coupon "FIRST15"
ORDER NOW

: Body Measurements.jmp (Help > Sample Data) Correlation Correlation is a measure of the linear association between two variables.

/in /by

Example: Socioeconomic.jmp (Help > Sample Data) Principal Component Analysis Use principal components analysis (PCA) to reduce the dimensionality of a data set. Principal Components 1. Select Analyze > Multivariate Methods > Principal Components. 2. Select continuous variables from Select Columns, and Click Y, Columns (continuous variables have blue triangles). 3. Click OK. By default, JMP® displays the eigenvalues and three Summary Plots (below, from left to right). • Eigenvalue Pareto Plot: The percent and cumulative total percent of the variation accounted for by each principal component. • Score Plot (middle): A scatterplot of the first two principal components. • Loading Plot: Correlations between the original variables and the first two principal components. (Note: The factor loadings are unrotated.) Tips: • By default, PCA is performed on correlations. • Click on the top red triangle to change the method of calculation, view additional results, save the principal components to the data table, or view detailed information associated with the eigenvalues. • Principal component analysis can also be accessed through the Scatterplot 3D platform or the Multivariate platform. Note: For more information about principal components analysis, see “principal components” in the JMP Help or see the book Multivariate Methods (under Help > Books). Interpretation: • The first two principal components account for 93.4% (57.5 + 35.9 = 93.4) of the total variation in the data (see the Pareto Plot). These numbers are displayed on the graph axes of the Score Plot and Loading Plot. • All of the original variables are positively correlated with the first principal component (see the Loading Plot). Total Population and Total Employment are positively correlated with the second principal component, while the other variables are negatively correlated with the second principal component. jmp.com/learn rev 02/2014

 
Looking for a Similar Assignment? Order now and Get 10% Discount! Use Coupon Code "Newclient"