Julia Wrobel

@juliawrobel4127 - 46 本の動画

チャンネル登録者数 777人

Assistant Professor of Biostatistics at Emory University

最近の動画

Week 3 OH 1:14:02

Week 3 OH

Tuesday, Feb 8 Lab 1:02:44

Tuesday, Feb 8 Lab

Thursday Feb 3 Office Hours 1:27:41

Thursday Feb 3 Office Hours

Tuesday, February 1 Lab Week 2 1:17:47

Tuesday, February 1 Lab Week 2

Office Hours Thursday January 27 38:15

Office Hours Thursday January 27

L13.3 Missing Data 20:23

L13.3 Missing Data

L13.2 GLMMs in R 25:47

L13.2 GLMMs in R

L13.1: Intro to Generalized Linear Mixed Models 11:52

L13.1: Intro to Generalized Linear Mixed Models

L12.3: GEEs for Binary and Count Data 19:36

L12.3: GEEs for Binary and Count Data

L12.2: GEEs for Continuous Data 18:05

L12.2: GEEs for Continuous Data

L12.1: Introduction to GEEs 20:53

L12.1: Introduction to GEEs

L11.4: Inference for LMMs 26:13

L11.4: Inference for LMMs

L11.3 Random Effects Models in R 33:04

L11.3 Random Effects Models in R

L11.2: Random Intercept and Slope Models 28:58

L11.2: Random Intercept and Slope Models

L11.1: Intro to Linear Mixed Models 9:51

L11.1: Intro to Linear Mixed Models

L10.4 EDA for LDA Part 2 18:04

L10.4 EDA for LDA Part 2

L10.3: Correlation Structures 29:50

L10.3: Correlation Structures

L10.2: EDA for the Mean 23:10

L10.2: EDA for the Mean

L10.1: Intro to LDA 10:46

L10.1: Intro to LDA

L9.4: Overdispersion in Count Data 27:45

L9.4: Overdispersion in Count Data

L9.3 Poisson Regression in R 22:38

L9.3 Poisson Regression in R

L9.2: Poisson Regression 24:41

L9.2: Poisson Regression

L9.1: Count Data 7:30

L9.1: Count Data

L7.1 Other link functions for binary data 46:44

L7.1 Other link functions for binary data

L6.4 Conditional Logistic Regression in R 8:46

L6.4 Conditional Logistic Regression in R

L6.3 Conditional Logistic Regression 12:01

L6.3 Conditional Logistic Regression

L6.2 Matched Case-Control Studies 13:13

L6.2 Matched Case-Control Studies

L6.1 Case-Control Studies 6:54

L6.1 Case-Control Studies

L5.4 Convergence problems in logistic regression 16:07

L5.4 Convergence problems in logistic regression

L5.3 Logistic Regression Diagnostics 8:35

L5.3 Logistic Regression Diagnostics

L5.2 Predictive Power 28:10

L5.2 Predictive Power

L5.1 Comparing Logistic Regression Models 16:03

L5.1 Comparing Logistic Regression Models

L4.3 Confounding 8:29

L4.3 Confounding

L4.4 Goodness of Fit Tests 32:40

L4.4 Goodness of Fit Tests

L4.2 Interaction 28:02

L4.2 Interaction

L4.1 Inference for Logistic Regression 17:37

L4.1 Inference for Logistic Regression

L3.3 Grouped and Ungrouped data 23:17

L3.3 Grouped and Ungrouped data

L3.2 Logistic Regression MLE 28:09

L3.2 Logistic Regression MLE

L3.1 Intro Logistic Regression 18:58

L3.1 Intro Logistic Regression

L2.2 Asymptotic Properties of MLEs 18:40

L2.2 Asymptotic Properties of MLEs

L2.4 Delta Method and Invariance of MLEs 8:26

L2.4 Delta Method and Invariance of MLEs

L2.3 LR, Wald, and Score Tests 22:21

L2.3 LR, Wald, and Score Tests

L2.1 MLEs 25:38

L2.1 MLEs

L1.2 26:04

L1.2

L1.3 Components of GLMs 7:52

L1.3 Components of GLMs

L1.1 19:29

L1.1