8. Statistics

• 8.1. Gang’s Stats Blog
• 8.2. When and how to center a variable?
  • 8.2.1. Major points
  • 8.2.2. Usage clarifications of “covariate”
  • 8.2.3. Purpose of modeling a quantitative covariate
• 8.3. How to get the degrees of freedom (DF) in an AFNI file?
  • 8.3.1. Using 3dinfo
  • 8.3.2. Using the AFNI GUI
  • 8.3.3. Using 3dAttribute
  • 8.3.4. Using p2dsetstat and dsetstat2p
• 8.4. Deconvolution Analysis of FMRI Time Series Data
  • 8.4.1. Abstract
  • 8.4.2. ... Full Enlightenment
• 8.5. Duration modulation (DM) blocks (3dDeconvolve)
  • 8.5.1. TL;DR description of block choices
  • 8.5.2. Longer description of block choices
  • 8.5.3. Additional notes
  • 8.5.4. Demo image script
• 8.6. Nonlinear Regression Analysis of FMRI Time Series Data (NLfim)
  • 8.6.1. Abstract
  • 8.6.2. ... Full Enlightenment
• 8.7. Notes on 3dREMLfit
  • 8.7.1. Background: time series censoring approaches
  • 8.7.2. Are the censoring approaches the same for 3dREMLfit?
  • 8.7.3. Attributes in the 3dREMLFIT *.xmat.1D format
• 8.8. Analysis of Variance (ANOVA)
  • 8.8.1. Abstract
  • 8.8.2. Program 3dANOVA
  • 8.8.3. ... Full Enlightenment
• 8.9. Multivariate modeling (MVM) approach for group analysis
  • 8.9.1. Introduction
  • 8.9.2. Why another group analysis program?
  • 8.9.3. How is 3dMVM compared to 3dANOVAx?
  • 8.9.4. How is 3dMVM compared to GroupAna?
  • 8.9.5. How is 3dMVM compared to 3dLME?
  • 8.9.6. 3dMVM usage
• 8.10. Linear Mixed-Effects (LME) Modeling
  • 8.10.1. Introduction
• 8.11. Mixed-Effects Meta Analysis (MEMA)
  • 8.11.1. Theoretical background
  • 8.11.2. MEMA Modeling
  • 8.11.3. How to use 3dMEMA to handle multiple groups
  • 8.11.4. Running 3dMEMA inside R
  • 8.11.5. Acknowledgements
• 8.12. False Discovery Rate (FDR)
  • 8.12.1. Program 3dFDR
  • 8.12.2. ... Full Enlightenment
• 8.13. Nonparametric Statistical Analysis of FMRI Data
  • 8.13.1. Abstract
  • 8.13.2. ... Full Enlightenment