F-tests: How to set up contrasts and how they relate to t-tests
mumfordbrainstats
F-tests: How to set up contrasts and how they relate to t-tests
13:11
6 Can the regularization in mixed models be problematic?
mumfordbrainstats
6 Can the regularization in mixed models be problematic?
23:41
5 Relationship between mixed model conditional modes (aka BLUPS) and OLS estimates
mumfordbrainstats
5 Relationship between mixed model conditional modes (aka BLUPS) and OLS estimates
18:48
4 Mixed models series: What is this regularization and why don't we analyze the data in 2 stages?
mumfordbrainstats
4 Mixed models series: What is this regularization and why don't we analyze the data in 2 stages?
22:07
3 Mixed models series: Simulating data using the 2-stage random effects formulation
mumfordbrainstats
3 Mixed models series: Simulating data using the 2-stage random effects formulation
18:02
2 Mixed models series: Two stage random effects formulation
mumfordbrainstats
2 Mixed models series: Two stage random effects formulation
19:25
1 Mixed model series: Regression review
mumfordbrainstats
1 Mixed model series: Regression review
13:14
0 Overview of the Mixed Model Series
mumfordbrainstats
0 Overview of the Mixed Model Series
3:54
confused by random effects structures in mixed models?
mumfordbrainstats
confused by random effects structures in mixed models?
20:28
Explaining and predicting
mumfordbrainstats
Explaining and predicting
18:04
How to use FEAT while skipping registration
mumfordbrainstats
How to use FEAT while skipping registration
10:28
Permutation tests: Why exchangeability fails to hold with bivariate outliers
mumfordbrainstats
Permutation tests: Why exchangeability fails to hold with bivariate outliers
10:30
Outliers: One simple step to help control false positives
mumfordbrainstats
Outliers: One simple step to help control false positives
14:30
Positive Predictive Value (PPV): What is it? How is it different from Type I error and Power?
mumfordbrainstats
Positive Predictive Value (PPV): What is it? How is it different from Type I error and Power?
16:31
PPI video 2: GPPI
mumfordbrainstats
PPI video 2: GPPI
17:25
PPI video 1: Deconvolution
mumfordbrainstats
PPI video 1: Deconvolution
15:44
Addressing bad practices
mumfordbrainstats
Addressing bad practices
21:11
FSL: Write your methods section in about 5 minutes!
mumfordbrainstats
FSL: Write your methods section in about 5 minutes!
18:25
QA for step 6: Group level analysis
mumfordbrainstats
QA for step 6: Group level analysis
6:13
Step 6: Group analysis
mumfordbrainstats
Step 6: Group analysis
7:20
QA for step 5: Level 2 analysis
mumfordbrainstats
QA for step 5: Level 2 analysis
13:20
Step 5: Run your level 2 analyses (when you have 3 levels)
mumfordbrainstats
Step 5: Run your level 2 analyses (when you have 3 levels)
11:30
QA for Step 4: Checking your level 1
mumfordbrainstats
QA for Step 4: Checking your level 1
16:57
Feat pipeline, Step 4, Part 2: Scripting your level 1 analyses
mumfordbrainstats
Feat pipeline, Step 4, Part 2: Scripting your level 1 analyses
13:43
A tour of the Feat directory files
mumfordbrainstats
A tour of the Feat directory files
20:05
Level 1 Feat Gui extras #5: Brief overview of boundary based registration
mumfordbrainstats
Level 1 Feat Gui extras #5: Brief overview of boundary based registration
12:42
Level 1 Feat Gui extras #4: Brief overview of image registration
mumfordbrainstats
Level 1 Feat Gui extras #4: Brief overview of image registration
20:46
Level 1 Feat Gui extras #3: Empty EV's
mumfordbrainstats
Level 1 Feat Gui extras #3: Empty EV's
5:53
Level 1 Feat GUI extras #2: Slice timing correction and motion correction
mumfordbrainstats
Level 1 Feat GUI extras #2: Slice timing correction and motion correction
13:45
Level 1 Feat Gui extras #1: keeping your directories organized
mumfordbrainstats
Level 1 Feat Gui extras #1: keeping your directories organized
4:32
Step 4, part 1: Setting up the Feat GUI
mumfordbrainstats
Step 4, part 1: Setting up the Feat GUI
30:13
Follow-up to step 3: What is framewise displacement?
mumfordbrainstats
Follow-up to step 3: What is framewise displacement?
20:19
Step 3: Prepare your BOLD data
mumfordbrainstats
Step 3: Prepare your BOLD data
27:18
Step 2: Brain extraction & QA
mumfordbrainstats
Step 2: Brain extraction & QA
17:29
QA #1: Check your newly created NIfTI files
mumfordbrainstats
QA #1: Check your newly created NIfTI files
19:29
Using FSL: Overview of what's to come
mumfordbrainstats
Using FSL: Overview of what's to come
11:02
Intro
mumfordbrainstats
Intro
1:44
Nonparametric thresholding tutorial using Randomise
mumfordbrainstats
Nonparametric thresholding tutorial using Randomise
25:26
Paper overview: Can parametric statistical methods be trusted?
mumfordbrainstats
Paper overview: Can parametric statistical methods be trusted?
25:46
Can we say one regressor is better than another?
mumfordbrainstats
Can we say one regressor is better than another?
14:15
How worrisome is your collinearity? Look at the VIF
mumfordbrainstats
How worrisome is your collinearity? Look at the VIF
12:22
Collinearity: Is orthogonalization the answer? (No)
mumfordbrainstats
Collinearity: Is orthogonalization the answer? (No)
18:36
How does collinearity impact type I error and power: An R illustration
mumfordbrainstats
How does collinearity impact type I error and power: An R illustration
19:25
Parametric Modulation (extra video in collinearity series)
mumfordbrainstats
Parametric Modulation (extra video in collinearity series)
27:13
Collinearity 1: What is it?
mumfordbrainstats
Collinearity 1: What is it?
16:17
Guide for best practices in data analysis: COBIDAS overview
mumfordbrainstats
Guide for best practices in data analysis: COBIDAS overview
34:43
Efficiency, Day 4: Smart ways to find efficient designs
mumfordbrainstats
Efficiency, Day 4: Smart ways to find efficient designs
17:01
Efficiency Day 3: Matlab example
mumfordbrainstats
Efficiency Day 3: Matlab example
25:22
Efficiency Day 2: The math behind the calculation
mumfordbrainstats
Efficiency Day 2: The math behind the calculation
9:50
Efficiency Day 1: Difference between estimation and detection
mumfordbrainstats
Efficiency Day 1: Difference between estimation and detection
10:56
Brief paper overview: Threshold free cluster enhancement
mumfordbrainstats
Brief paper overview: Threshold free cluster enhancement
23:33
Day 32: Permutation tests that control for multiple comparisons
mumfordbrainstats
Day 32: Permutation tests that control for multiple comparisons
19:08
Day 31: Introduction to permutation tests
mumfordbrainstats
Day 31: Introduction to permutation tests
11:37
Day 30: Voxelwise Random Field Theory
mumfordbrainstats
Day 30: Voxelwise Random Field Theory
18:08
Day 29: Why Bonferroni doesn't work well in imaging
mumfordbrainstats
Day 29: Why Bonferroni doesn't work well in imaging
14:07
Day 28: Why our smooth data makes counting false positives tricky
mumfordbrainstats
Day 28: Why our smooth data makes counting false positives tricky
11:35
Day 27: Different types of error rates
mumfordbrainstats
Day 27: Different types of error rates
12:15
Day 26: Clusters, peaks and voxels
mumfordbrainstats
Day 26: Clusters, peaks and voxels
9:33
Day 25 : Recap of Type I and Type II errors
mumfordbrainstats
Day 25 : Recap of Type I and Type II errors
9:50
Day 24: When fixed effects models are good
mumfordbrainstats
Day 24: When fixed effects models are good
14:03
Day 23: SPM/FSL/AFNI differences in mixed model estimation
mumfordbrainstats
Day 23: SPM/FSL/AFNI differences in mixed model estimation
15:54
Day 22: Mixed effects models applied to fMRI data
mumfordbrainstats
Day 22: Mixed effects models applied to fMRI data
10:50
Day 21: The 2 stage summary statics model
mumfordbrainstats
Day 21: The 2 stage summary statics model
10:45
Day 20: Mixed model motivation..the hair example
mumfordbrainstats
Day 20: Mixed model motivation..the hair example
12:33
Day 19: Other level 1 modeling considerations
mumfordbrainstats
Day 19: Other level 1 modeling considerations
9:39
Day 18: Prewhitening
mumfordbrainstats
Day 18: Prewhitening
18:09
Day 17: Highpass filtering data
mumfordbrainstats
Day 17: Highpass filtering data
16:38
Day 16: Finite impulse response (FIR) and constrained basis sets
mumfordbrainstats
Day 16: Finite impulse response (FIR) and constrained basis sets
9:46
Day 15: Level 1 modeling, the canonical HRF
mumfordbrainstats
Day 15: Level 1 modeling, the canonical HRF
16:34
Paper Overview: Simultaneous control of error rates in fMRI data analysis
mumfordbrainstats
Paper Overview: Simultaneous control of error rates in fMRI data analysis
23:04
Day 14: 2-Way ANOVA, 2ways
mumfordbrainstats
Day 14: 2-Way ANOVA, 2ways
12:33
Day 13: 1 way ANOVA factor effects setup
mumfordbrainstats
Day 13: 1 way ANOVA factor effects setup
12:35
12: 1 way ANOVA cell means approach
mumfordbrainstats
12: 1 way ANOVA cell means approach
11:22
Day 11: Paired t-test
mumfordbrainstats
Day 11: Paired t-test
9:26
Day 10 Two Sample T-test, 3 ways
mumfordbrainstats
Day 10 Two Sample T-test, 3 ways
10:44
Day 9: One sample T-test. Why does a column of 1's model the mean?
mumfordbrainstats
Day 9: One sample T-test. Why does a column of 1's model the mean?
13:20
Day 8: Mean centering regressors
mumfordbrainstats
Day 8: Mean centering regressors
11:39
Day 7: Interpreting linear regression parameters
mumfordbrainstats
Day 7: Interpreting linear regression parameters
8:40
Day 6: Contrasts in linear models
mumfordbrainstats
Day 6: Contrasts in linear models
13:01
Day 5: Hypothesis testing
mumfordbrainstats
Day 5: Hypothesis testing
14:47
Day 4: Multiple linear regression with matrices
mumfordbrainstats
Day 4: Multiple linear regression with matrices
12:14
Day 3: Matrix algebra overview
mumfordbrainstats
Day 3: Matrix algebra overview
11:55
Day 2: Simple linear regression
mumfordbrainstats
Day 2: Simple linear regression
12:24
Day 1: Basic statistics lingo
mumfordbrainstats
Day 1: Basic statistics lingo
13:43
Introduction to the summer cram session
mumfordbrainstats
Introduction to the summer cram session
10:40
Introduction to this channel and what I hope to achieve here
mumfordbrainstats
Introduction to this channel and what I hope to achieve here
12:56