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--- inspect3d:documentation:knowledge_discovery:principal_component_analysis [2024/06/19 13:53] – created sgranger
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-{{I3DCleanData2.png}}
-Inspect3D provides a number of ways to visualize and interact with the results of PCA. An overview of all PCA visualizes can be found in [[Inspect3D:Documentation:Pages:PCA_Graphs|Inspect3D PCA Graphs]].
-This page includes:
-  * Visualizing the [[Inspect3D:Documentation:Pages:PCA_Graphs#Variance_Explained|variance explained by each PC individually]];
-  * Visualizing the [[Inspect3D:Documentation:Pages:PCA_Graphs#Loading_Vector|variance explained by each PC at each point in the signal's cycle]];
-  * [[Inspect3D:Documentation:Pages:PCA_Graphs#Subject_Scores|Scatter-plotting workspace scores]] in PC-space;
-  * Showing the [[Inspect3D:Documentation:Pages:PCA_Graphs#Group_Scores|distribution of scores by group]] for each PC;
-  * Visualizing the [[Inspect3D:Documentation:Pages:PCA_Graphs#Extreme_Plot|mean and extreme values]] that result from reconstructing the underlying data with each PC; and
-  * Visualizing how the signals can be [[Inspect3D:Documentation:Pages:PCA_Graphs#PC_Reconstruction|reconstructed from the computed PCs]].
-===== Tutorials =====
-For a step-by-step example of how to use Inspect3D to perform PCA on your data, see the [[Inspect3D:Tutorials:Perform_Principal_Component_Analysis|PCA Tutorial]].
-For a step-by-step example of how to use Inspect3D to perform further statistical testing on PCA results, see the [[Inspect3D:Tutorials:Run_K-Means|Tutorial: Run K-Means]].
-For a step-by-step example of processing and analyzing large data sets in Inspect3D and using PCA to distinguish between groups, see the [[Inspect3D:Tutorials:Treadmill_Walking_In_Healthy_Individuals|Tutorial: Treadmill Walking In Healthy Individuals]] and the [[Inspect3D:Tutorials:Analysis_of_Baseball_Hitters|Tutorial: Analysis of Baseball Hitters]].