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Lec 12E, Repeated measures, advanced methods

Using (polnomial) regression models for the time dependence and howto in R.

Date: 27/11/2014 16:04 - Duration: 00:22:59 - Views: 397

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Lec 12D, Repeated measures, advanced methods

The semi-variogram and how to plot it in R.

Date: 27/11/2014 16:03 - Duration: 00:08:21 - Views: 298

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Lec 12C, Repeated measures, advanced methods

Other correlation structures and strategy of analysis.

Date: 27/11/2014 16:02 - Duration: 00:06:44 - Views: 288

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Lec 12B, Repeated measures, advanced methods

Gaussian spatial correlation model.

Date: 27/11/2014 16:00 - Duration: 00:08:17 - Views: 331

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Lec 12A, Repeated measures, advanced methods

The compound symmetry structure.

Date: 27/11/2014 15:59 - Duration: 00:07:19 - Views: 323

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RTutorial, Lec 11, Repeated measures, simple methods

How to plot and do simple analysis in R.

Date: 27/11/2014 15:58 - Duration: 00:17:04 - Views: 292

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Lec 11, Repeated measures, simple methods

Analysis by time, summary measure and simple random effect model.

Date: 27/11/2014 15:57 - Duration: 00:21:15 - Views: 335

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Lec 10J: Theory, part II. Prediction of random effects

See how they are shrinkage versions of the fixed effects equivalents.

Date: 20/11/2014 14:38 - Duration: 00:03:47 - Views: 276

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Lec 10I: Theory, part II. Expected Mean Squares

How to compute EMS's.

Date: 20/11/2014 14:37 - Duration: 00:07:18 - Views: 264

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Lec 10H: Theory, part II. REML Profile Likelihood.

Example: One-way ANOVA with random effect.

Date: 20/11/2014 14:36 - Duration: 00:05:47 - Views: 281

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Lec 10F: Theory, part II. The REML Likelihood.

Why - How?

Date: 20/11/2014 14:34 - Duration: 00:12:55 - Views: 283

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Lec 10G: Theory, part II. REML likeilhood

Example: One-way ANOVA with random effect.

Date: 20/11/2014 14:34 - Duration: 00:04:14 - Views: 268

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Lec 10E: Theory, part II. Profile Likelihood.

The idea of profile likelihood and using that for confidence intervals.

Date: 20/11/2014 14:32 - Duration: 00:06:59 - Views: 278

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Lec 10D: Theory, part II. Likelihood confidence intervals

CI's derived directly from the likelihood.

Date: 20/11/2014 14:31 - Duration: 00:03:00 - Views: 264

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Lec 10C: Theory, part II. Wald confidence intervals

The classic asymptotic (large sample) likelihood based confidence intervals.

Date: 20/11/2014 14:30 - Duration: 00:05:35 - Views: 267

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Lec 10V: Theory, part II. Model testing by likelihood.

The likelihood ratio statistic.

Date: 20/11/2014 14:28 - Duration: 00:08:15 - Views: 291

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Lec 10A: Theory, part II. The likelihood function

Example: One-sample normal data.

Date: 20/11/2014 14:27 - Duration: 00:20:27 - Views: 285

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R-TUTORIAL 9: Random Coefficient Analysis

How to do it in R: Focus on the Carrots data.

Date: 13/11/2014 11:42 - Duration: 00:28:05 - Views: 310

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Lec 9B: Random Coefficient Models, Real case.

Analysing carrots data: Consumer Preference Mapping.

Date: 13/11/2014 11:30 - Duration: 00:31:17 - Views: 316

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Lec 9A: Random Coefficient Models, Basic.

Motivation. Constructed data.

Date: 13/11/2014 11:29 - Duration: 00:30:25 - Views: 323

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Lec 8B: Analysis of covariance

A small (worst case) example of strong treatment group covariate differences.

Date: 31/10/2014 16:27 - Duration: 00:05:41 - Views: 173

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Lec 8A: Analysis of covariance

Basics: First linear models, then mixed models with blocks.

Date: 31/10/2014 16:26 - Duration: 00:26:36 - Views: 302

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R-TUTORIAL 8: Analysis of Covariance

How to do mixed model ANCOVA in R.

Date: 31/10/2014 16:25 - Duration: 00:21:44 - Views: 174

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Lec 8C: Analysis of covariance

Different slopes case and perspectives.

Date: 31/10/2014 16:24 - Duration: 00:26:16 - Views: 279

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R-TUTORIAL 7: Split-plot analysis

How to do split-plot in R.

Date: 26/10/2014 11:33 - Duration: 0:09:15 - Views: 432

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Lec 7C: Split-plot in perspective

Including more effects and the basics for repeated measures analysis.

Date: 26/10/2014 11:31 - Duration: 0:16:24 - Views: 433

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Lec 7B: Split-plot design data with Blocks

The structure and analysis of blocked split-plot data.

Date: 26/10/2014 11:29 - Duration: 0:06:05 - Views: 447

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Lec 7A: Basic split-plot design data

The structure and analysis of basic split-plot data.

Date: 26/10/2014 11:27 - Duration: 0:22:57 - Views: 460

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Lec 6G. The planks case, final.

How to summarize/post hoc analyze a significant interaction.

Date: 03/10/2014 12:09 - Duration: 0:15:22 - Views: 444

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Lec 6F: MIXED linear model diagnostics

How to do everything we just did for the linear models for MIXED linear models.

Date: 03/10/2014 12:08 - Duration: 0:11:09 - Views: 452

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Lec 6E: Back transformation

How to back transform the results of transformed data analysis.

Date: 03/10/2014 12:07 - Duration: 0:03:45 - Views: 444

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Lec 6D: Box-Cox transformations

Box-cox transformation in R.

Date: 03/10/2014 12:06 - Duration: 0:04:24 - Views: 455

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Linear model diagnostics

Outliers and influential observations.

Date: 03/10/2014 12:03 - Duration: 0:12:26 - Views: 461

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Lec 6B: Extended model for example.

Full model for log-transformed humidity measurements in planks data.

Date: 03/10/2014 12:03 - Duration: 0:02:56 - Views: 450

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Lec 6A: Linear Model diagnostics, part 1

Residual defintions and investigations.

Date: 03/10/2014 12:01 - Duration: 0:20:16 - Views: 477

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R-TUTORIAL 5: lactase data

Focus on variances

Date: 25/09/2014 13:17 - Duration: 0:13:31 - Views: 458

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Lec 5F: Comparing variance structures and CIs for variances

Focus on the variances

Date: 25/09/2014 13:15 - Duration: 0:08:54 - Views: 478

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Lec 5E: The three-layer model

The proper model for the lactase data

Date: 25/09/2014 13:13 - Duration: 0:15:07 - Views: 474

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Lec 5D: The two-layer model

The simplest REAL hierarchical model

Date: 25/09/2014 13:13 - Duration: 0:12:23 - Views: 483

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Lec 5C: Type I and Type III ANOVA again

A short overview of this.

Date: 25/09/2014 13:12 - Duration: 0:05:43 - Views: 460

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Lec 5A: Data with hierarchical structures

The lactase example introduced

Date: 25/09/2014 13:11 - Duration: 0:06:00 - Views: 449

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Lec 5B: The one-layer model

The (fixed) linear model

Date: 25/09/2014 13:11 - Duration: 0:03:05 - Views: 450

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R-TUTORIAL 3: Planks case story

Data import, explorative plotting, modelling and post hoc.

Date: 18/09/2014 14:37 - Duration: 0:24:22 - Views: 547

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Lec 3D. Testing of fixed effects - a recap.

REMLand ML and F-testings.

Date: 18/09/2014 13:07 - Duration: 0:06:28 - Views: 481

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Lec 3C: The overall mixed model approach.

How do we approach analysing data (with mixed linear models).

Date: 18/09/2014 13:02 - Duration: 0:10:05 - Views: 485

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Lec 3B. Planks case story: Modelling and post hoc.

Second part of the planks case story. (First part: "structure and explorative analysis" is not recorded)

Date: 18/09/2014 13:01 - Duration: 0:16:42 - Views: 526

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Lec 4A: The design matrix for linear models

What is a design matrix for a linear model?

Date: 12/09/2014 15:12 - Duration: 00:07:11 - Views: 334

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Lec 4F: Confidence intervals of fixed effects

Wald based.

Date: 11/09/2014 15:15 - Duration: 0:03:30 - Views: 402

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R-TUTORIAL 4B: Testing fixed and random effects

Using lmerTest. AND profile confidence intervals for random effect parameters.

Date: 11/09/2014 15:12 - Duration: 0:09:21 - Views: 477

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R-TUTORIAL 4A: Parametrizations, contrasts and LSMEANS

R sets the first level to zer0. What is an LSMEANS?

Date: 11/09/2014 15:10 - Duration: 0:14:25 - Views: 497

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Lec 4H: Mixed modelling - the overall approach

How to generally approach the data analysis based on a mixed model.

Date: 11/09/2014 15:08 - Duration: 0:13:38 - Views: 460

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Lec 4G: Test and confidence intervals for random effects

REML based testing of random effects and profile likelihood based confidence intervals for random effect parameters.

Date: 11/09/2014 15:08 - Duration: 0:08:47 - Views: 478

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Lec 4E: Partial and sequential ANOVA tables

What is a Type I and Type III ANOVA table?

Date: 11/09/2014 15:06 - Duration: 0:10:32 - Views: 469

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Lec 4F: Confidence intervals of fixed effects

Wald based confidence intervals for fixed effects.

Date: 11/09/2014 15:05 - Duration: 0:03:30 - Views: 451

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Lec 4D: Testing of fixed effects

Linear hypotheses and tests thereof in mixed linear models.

Date: 11/09/2014 15:03 - Duration: 0:16:24 - Views: 479

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Lec 4C: The (Restricted) maximum likelihood approach

Maximum Likelihood (ML) and Restricted Maximum Likelihood (REML) for mixed linear models.

Date: 11/09/2014 15:01 - Duration: 0:24:35 - Views: 528

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Lec 4B: The mixed model, definition

The mixed model, mean and variance-structure

Date: 11/09/2014 14:59 - Duration: 0:14:06 - Views: 548

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Rtutorial1C

Mixed model in R: lmer, post hoc and lmerTest.

Date: 04/09/2014 21:44 - Duration: 0:15:55 - Views: 510

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Rtutorial1B

Fixed 2-way ANOVA: lsmeans.

Date: 04/09/2014 21:43 - Duration: 0:10:47 - Views: 485

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Rtutorial1A

R intro, fixed 2-way ANOVA, lm, anova and Anova, lsmeans.

Date: 04/09/2014 21:40 - Duration: 0:22:39 - Views: 545

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Lec2

Factor Structure Diagrams

Date: 04/09/2014 21:38 - Duration: 0:27:29 - Views: 570

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Lec1C

Randomized block with missing data example. Recovery of inter block information.

Date: 04/09/2014 21:36 - Duration: 0:15:00 - Views: 560

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Lec1B: Introduction to mixed models

Simple Example, paired t-test, Randomized blocksand the mixed model.

Date: 04/09/2014 15:58 - Duration: 0:32:12 - Views: 761

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Lec1A: Course Intro

Practical Info

Date: 04/09/2014 09:23 - Duration: 0:11:35 - Views: 682

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Total duration: 14:09:15