Overview for Principal Components Analysis - Minitab
Use Principal Components Analysis to identify a smaller number of uncorrelated variables, called "principal components", from a large set of data. With this analysis, you create new variables …
Interpret all statistics and graphs for - Minitab
Find definitions and interpretation guidance for every statistic and graph that is provided with the principal components analysis.
Interpret the key results for - Minitab
Complete the following steps to interpret a principal components analysis. Key output includes the eigenvalues, the proportion of variance that the component explains, the coefficients, and …
Example of Principal Components Analysis - Minitab
A bank requires eight pieces of information from loan applicants: income, education level, age, length of time at current residence, length of time with current employer, savings, debt, and …
Select the graphs for Principal Components Analysis - Minitab
Stat > Multivariate > Principal Components > Graphs Scree plot Use a scree plot to identify the number of components that explain most of the variation in the data. Score plot for first 2 …
Enter your data for Principal Components Analysis - Minitab
Specify the data for your analysis, enter the number of components to calculate, and specify the type of matrix.
Store statistics for Principal Components Analysis - Minitab
Statistics that you can store Coefficients Enter the columns to store the coefficients (loadings) of the principal components, one row for each variable and one column for each component. The …
What are the differences between principal components analysis …
Principal components analysis and factor analysis are similar because both analyses are used to simplify the structure of a set of variables. However, the analyses differ in several important …
Methods and formulas for Factor Analysis - Minitab
An orthogonal rotation is an orthogonal transformation of the factor loadings that allows for easier interpretation of the factor loadings. The rotated loadings retain the correlation or covariance …
Interpret the key results for Multiple Correspondence Analysis
Complete the following steps to interpret a multiple correspondence analysis. Key output includes principal components, inertia, proportion of inertia, quality, mass, and column plot.