Find definitions and interpretation for every statistic that is provided in the analysis of contingency table for simple correspondence analysis.

Minitab calculates each principle axis, which is also called a principal component. Minitab orders the principal components by how much they account for the total inertia. The first principal component (axis) accounts for the most inertia. The second principal component (axis) accounts for most of the remaining inertia, and so on.

Use the principle axes to evaluate which components account for most of the variability in the data.

Cell inertia is the chi-squared value in the cell divided by the total frequency for the contingency table. The row inertia is the sum of the cell inertias for the row. The column inertia is the sum of the cell inertias for the column. The sum of all the cell inertias is the total inertia, or simply the inertia.

Use inertia to assess associations between categories and contributions to variation in the data. Higher values generally indicate a stronger association and greater variation. You can also use inertia to determine which principal components account for most of the deviation from the expected values in the data.

The proportion indicates the proportion of the total inertia (the inertia explained by all the components) that each principal component (axis) explains. Minitab displays the components in order of their proportions, from greatest to least. Each proportion is visually represented in the histogram.

The cumulative proportion indicates the cumulative sum of the proportions as components (axes) are added.

Use the proportion and cumulative proportion to help determine how many components are sufficient to explain most of the total inertia. Ideally, two or three components account for most of the total inertia and are more important than the other components.