sift:tutorials:perform_statistical_parametric_mapping
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sift:tutorials:perform_statistical_parametric_mapping [2024/12/17 18:12] – [GLM] wikisysop | sift:tutorials:perform_statistical_parametric_mapping [2024/12/17 18:27] (current) – [Analysis] wikisysop | ||
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- For unregistered data: | - For unregistered data: | ||
* SPM Name: SPM | * SPM Name: SPM | ||
- | * Statistic: | + | * Statistic: |
* Group 1: OA | * Group 1: OA | ||
* Group 2: NC | * Group 2: NC | ||
+ | * Threshold: 0.05 | ||
+ | * Two-Tailed: Checked | ||
=== Registered Data === | === Registered Data === | ||
Line 137: | Line 139: | ||
- For registered data: | - For registered data: | ||
* SPM Name: SPM_Registered | * SPM Name: SPM_Registered | ||
- | * Statistic: | + | * Statistic: |
* Group 1: OA_Registered | * Group 1: OA_Registered | ||
* Group 2: NC_Registered | * Group 2: NC_Registered | ||
+ | * Threshold: 0.05 | ||
+ | * Two-Tailed: Checked | ||
- | We have now calculated two SPMs, which we can easily compare/ | + | We have now calculated two SPMs, which we can easily compare/ |
{{: | {{: | ||
- | The difference between both SPMs is most apparent at ~65% of the gait cycle. Here we can see a significantly more pronounced t statistic (~10 vs ~12.5). While both are well above the specified threshold where alpha=0.01, this can show us how curve registration can be useful to correctly align our data, and get more meaningful results from our analysis. | + | The difference between both SPMs is most apparent at ~65% of the gait cycle. Here we can see a significantly more pronounced t statistic (~10 vs ~12.5). While both are well above the specified threshold where alpha=0.05, this can show us how curve registration can be useful to correctly align our data, and get more meaningful results from our analysis. |
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sift/tutorials/perform_statistical_parametric_mapping.1734459163.txt.gz · Last modified: 2024/12/17 18:12 by wikisysop