9. Statistics

• 9.1. Gang’s Stats Blog
• 9.2. When and how to center a variable?
  • 9.2.1. Major points
  • 9.2.2. Usage clarifications of “covariate”
  • 9.2.3. Purpose of modeling a quantitative covariate
• 9.3. How to get the degrees of freedom (DF) in an AFNI file?
  • 9.3.1. Using 3dinfo
  • 9.3.2. Using the AFNI GUI
  • 9.3.3. Using 3dAttribute
  • 9.3.4. Using p2dsetstat and dsetstat2p
• 9.4. Deconvolution Analysis of FMRI Time Series Data
  • 9.4.1. Abstract
  • 9.4.2. ... Full Enlightenment
• 9.5. Duration modulation (DM) blocks (3dDeconvolve)
  • 9.5.1. TL;DR description of block choices
  • 9.5.2. Longer description of block choices
  • 9.5.3. Additional notes
  • 9.5.4. Demo image script
• 9.6. Nonlinear Regression Analysis of FMRI Time Series Data (NLfim)
  • 9.6.1. Abstract
  • 9.6.2. ... Full Enlightenment
• 9.7. Notes on 3dREMLfit
  • 9.7.1. Background: time series censoring approaches
  • 9.7.2. Are the censoring approaches the same for 3dREMLfit?
  • 9.7.3. Attributes in the 3dREMLFIT *.xmat.1D format
• 9.8. Analysis of Variance (ANOVA)
  • 9.8.1. Abstract
  • 9.8.2. Program 3dANOVA
  • 9.8.3. ... Full Enlightenment
• 9.9. Multivariate modeling (MVM) approach for group analysis
  • 9.9.1. Introduction
  • 9.9.2. Why another group analysis program?
  • 9.9.3. How is 3dMVM compared to 3dANOVAx?
  • 9.9.4. How is 3dMVM compared to GroupAna?
  • 9.9.5. How is 3dMVM compared to 3dLME?
  • 9.9.6. 3dMVM usage
• 9.10. Linear Mixed-Effects (LME) Modeling
  • 9.10.1. Introduction
• 9.11. Mixed-Effects Meta Analysis (MEMA)
  • 9.11.1. Theoretical background
  • 9.11.2. MEMA Modeling
  • 9.11.3. How to use 3dMEMA to handle multiple groups
  • 9.11.4. Running 3dMEMA inside R
  • 9.11.5. Acknowledgements
• 9.12. False Discovery Rate (FDR)
  • 9.12.1. Program 3dFDR
  • 9.12.2. ... Full Enlightenment
• 9.13. Nonparametric Statistical Analysis of FMRI Data
  • 9.13.1. Abstract
  • 9.13.2. ... Full Enlightenment