Канал: mumfordbrainstats

F-tests: How to set up contrasts and how they relate to t-tests

F-tests: How to set up contrasts and how they relate to t-tests

6 Can the regularization in mixed models be problematic?

6 Can the regularization in mixed models be problematic?

5 Relationship between mixed model conditional modes (aka BLUPS) and OLS estimates

5 Relationship between mixed model conditional modes (aka BLUPS) and OLS estimates

4 Mixed models series: What is this regularization and why don't we analyze the data in 2 stages?

4 Mixed models series: What is this regularization and why don't we analyze the data in 2 stages?

3 Mixed models series: Simulating data using the 2-stage random effects formulation

3 Mixed models series: Simulating data using the 2-stage random effects formulation

2 Mixed models series: Two stage random effects formulation

2 Mixed models series: Two stage random effects formulation

1 Mixed model series: Regression review

1 Mixed model series: Regression review

0 Overview of the Mixed Model Series

0 Overview of the Mixed Model Series

confused by random effects structures in mixed models?

confused by random effects structures in mixed models?

Explaining and predicting

Explaining and predicting

How to use FEAT while skipping registration

How to use FEAT while skipping registration

Permutation tests: Why exchangeability fails to hold with bivariate outliers

Permutation tests: Why exchangeability fails to hold with bivariate outliers

Outliers: One simple step to help control false positives

Outliers: One simple step to help control false positives

Positive Predictive Value (PPV): What is it? How is it different from Type I error and Power?

Positive Predictive Value (PPV): What is it? How is it different from Type I error and Power?

PPI video 2: GPPI

PPI video 2: GPPI

PPI video 1: Deconvolution

PPI video 1: Deconvolution

Addressing bad practices

Addressing bad practices

FSL: Write your methods section in about 5 minutes!

FSL: Write your methods section in about 5 minutes!

QA for step 6: Group level analysis

QA for step 6: Group level analysis

Step 6: Group analysis

Step 6: Group analysis

QA for step 5: Level 2 analysis

QA for step 5: Level 2 analysis

Step 5: Run your level 2 analyses (when you have 3 levels)

Step 5: Run your level 2 analyses (when you have 3 levels)

QA for Step 4: Checking your level 1

QA for Step 4: Checking your level 1

Feat pipeline, Step 4, Part 2: Scripting your level 1 analyses

Feat pipeline, Step 4, Part 2: Scripting your level 1 analyses

A tour of the Feat directory files

A tour of the Feat directory files

Level 1 Feat Gui extras #5: Brief overview of boundary based registration

Level 1 Feat Gui extras #5: Brief overview of boundary based registration

Level 1 Feat Gui extras #4: Brief overview of image registration

Level 1 Feat Gui extras #4: Brief overview of image registration

Level 1 Feat Gui extras #3: Empty EV's

Level 1 Feat Gui extras #3: Empty EV's

Level 1 Feat GUI extras #2: Slice timing correction and motion correction

Level 1 Feat GUI extras #2: Slice timing correction and motion correction

Level 1 Feat Gui extras #1: keeping your directories organized

Level 1 Feat Gui extras #1: keeping your directories organized

Step 4, part 1: Setting up the Feat GUI

Step 4, part 1: Setting up the Feat GUI

Follow-up to step 3: What is framewise displacement?

Follow-up to step 3: What is framewise displacement?

Step 3: Prepare your BOLD data

Step 3: Prepare your BOLD data

Step 2: Brain extraction & QA

Step 2: Brain extraction & QA

QA #1: Check your newly created NIfTI files

QA #1: Check your newly created NIfTI files

Using FSL: Overview of what's to come

Using FSL: Overview of what's to come

Intro

Intro

Nonparametric thresholding tutorial using Randomise

Nonparametric thresholding tutorial using Randomise

Paper overview: Can parametric statistical methods be trusted?

Paper overview: Can parametric statistical methods be trusted?

Can we say one regressor is better than another?

Can we say one regressor is better than another?

How worrisome is your collinearity? Look at the VIF

How worrisome is your collinearity? Look at the VIF

Collinearity: Is orthogonalization the answer? (No)

Collinearity: Is orthogonalization the answer? (No)

How does collinearity impact type I error and power: An R illustration

How does collinearity impact type I error and power: An R illustration

Parametric Modulation (extra video in collinearity series)

Parametric Modulation (extra video in collinearity series)

Collinearity 1: What is it?

Collinearity 1: What is it?

Guide for best practices in data analysis: COBIDAS overview

Guide for best practices in data analysis: COBIDAS overview

Efficiency, Day 4: Smart ways to find efficient designs

Efficiency, Day 4: Smart ways to find efficient designs

Efficiency Day 3: Matlab example

Efficiency Day 3: Matlab example

Efficiency Day 2: The math behind the calculation

Efficiency Day 2: The math behind the calculation

Efficiency Day 1: Difference between estimation and detection

Efficiency Day 1: Difference between estimation and detection

Brief paper overview: Threshold free cluster enhancement

Brief paper overview: Threshold free cluster enhancement

Day 32: Permutation tests that control for multiple comparisons

Day 32: Permutation tests that control for multiple comparisons

Day 31: Introduction to permutation tests

Day 31: Introduction to permutation tests

Day 30: Voxelwise Random Field Theory

Day 30: Voxelwise Random Field Theory

Day 29: Why Bonferroni doesn't work well in imaging

Day 29: Why Bonferroni doesn't work well in imaging

Day 28: Why our smooth data makes counting false positives tricky

Day 28: Why our smooth data makes counting false positives tricky

Day 27: Different types of error rates

Day 27: Different types of error rates

Day 26: Clusters, peaks and voxels

Day 26: Clusters, peaks and voxels

Day 25 : Recap of Type I and Type II errors

Day 25 : Recap of Type I and Type II errors

Day 24: When fixed effects models are good

Day 24: When fixed effects models are good

Day 23: SPM/FSL/AFNI differences in mixed model estimation

Day 23: SPM/FSL/AFNI differences in mixed model estimation

Day 22: Mixed effects models applied to fMRI data

Day 22: Mixed effects models applied to fMRI data

Day 21: The 2 stage summary statics model

Day 21: The 2 stage summary statics model

Day 20: Mixed model motivation..the hair example

Day 20: Mixed model motivation..the hair example

Day 19: Other level 1 modeling considerations

Day 19: Other level 1 modeling considerations

Day 18: Prewhitening

Day 18: Prewhitening

Day 17: Highpass filtering data

Day 17: Highpass filtering data

Day 16: Finite impulse response (FIR) and constrained basis sets

Day 16: Finite impulse response (FIR) and constrained basis sets

Day 15: Level 1 modeling, the canonical HRF

Day 15: Level 1 modeling, the canonical HRF

Paper Overview: Simultaneous control of error rates in fMRI data analysis

Paper Overview: Simultaneous control of error rates in fMRI data analysis

Day 14: 2-Way ANOVA, 2ways

Day 14: 2-Way ANOVA, 2ways

Day 13: 1 way ANOVA factor effects setup

Day 13: 1 way ANOVA factor effects setup

12: 1 way ANOVA cell means approach

12: 1 way ANOVA cell means approach

Day 11: Paired t-test

Day 11: Paired t-test

Day 10 Two Sample T-test, 3 ways

Day 10 Two Sample T-test, 3 ways

Day 9: One sample T-test. Why does a column of 1's model the mean?

Day 9: One sample T-test. Why does a column of 1's model the mean?

Day 8: Mean centering regressors

Day 8: Mean centering regressors

Day 7: Interpreting linear regression parameters

Day 7: Interpreting linear regression parameters

Day 6: Contrasts in linear models

Day 6: Contrasts in linear models

Day 5: Hypothesis testing

Day 5: Hypothesis testing

Day 4: Multiple linear regression with matrices

Day 4: Multiple linear regression with matrices

Day 3: Matrix algebra overview

Day 3: Matrix algebra overview

Day 2: Simple linear regression

Day 2: Simple linear regression

Day 1: Basic statistics lingo

Day 1: Basic statistics lingo

Introduction to the summer cram session

Introduction to the summer cram session

Introduction to this channel and what I hope to achieve here

Introduction to this channel and what I hope to achieve here