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No
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Main subject
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More description
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1
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Chemometrics intro
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A couple of
motivating examples (26 minutes) (2012)
Some
basic algebra and centering/scaling (35 minutes)(2011)
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2
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Multiple
Linear Regression (MLR)
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Multiple
Linear
Regression, Part1 (44 minutes)(2012)
Multiple
Linear
Regression, Part2 (58 minutes) (2012)
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3
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Principal
Component Regression (PCR)
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Principal
Component Regression, Part 1 (27 minutes)
Principal
Component Regression, Part 2 (38 minutes)
Principal
Component Regression Unscrambler Tutorial
(24 minutes)
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4
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Partial
Least Squares Regression (PLSR)
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Partial Least
Squares Regression, Part 1 (33 minutes)
Partial
Least Squares Regression, Part 2 (18 minutes)
Partial
Least Squares Regression, Unscrambler Tutorial (38 minutes)
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5
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Ridge
Regression
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Ridge Regression,
Part 1 (36 minutes)
Ridge Regression,
Part 2 (26 minutes)
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6
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Classification
(LDA,
QDA, PLS-DA, other methods)
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Classification,
Part 1 (31 minutes)
Classification,
Part 2 (51 minutes)
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7
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Support
Vector Machines
(SVM)
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A
very brief (and superficial) intro to Optimal Separating Hyperplanes
(OSH) and SVMs (21 minutes)
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8
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Multivariate
Analysis of Variance
(MANOVA)
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Introduction to MANOVA (A pps-file)
(from
a previous course)
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10
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Introduction
to Mixed Models. Randomized blocks
design
(Module 1)
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Course 02429
Intro (15 minutes)
General
introduction to mixed models (45 minutes)
R introduction,
2010
(30 minutes)
R
introduction, 2011
(43 minutes)
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11
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Factor structure
diagrams
(Module 2)
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Factor
structure diagrams, 2010 (30 minutes)
Factor
structure diagrams, part1, 2011 (8 minutes)
Factor
structure diagrams, part2 2011 (16 minutes)
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12
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A case study
(Module 3)
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Drying of beech
wood - a case study, a preamble (5 minutes)
Drying of beech
wood - a case study, cont. (36 minutes)
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13
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Mixed model
theory, part I.(Module 4)
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Mixed model
theory, part I (47 minutes)
R tutorial, module 3 (and 4) (36 minutes)
TypeI and Type
III ANOVA Tables, 2011 (21 minutes)
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14
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Hierarchical random effects (Module 5)
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Hierarchical
random effects, Part 1 (35 minutes)
Hierarchical
random effects, Part 2 (45 minutes)
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15
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Model diagnostics
(Module 6)
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Model
diagnostics, 2011 (63 minutes)
Model diagnostics – R tutorial, 2011 (21 minutes)
Model diagnostics – R tutorial, 2010 (32 minutes)
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16
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The analysis of
split-plot design data (Module 7)
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The analysis of
split-plot design data, Part 1 (18 minutes)
The analysis of
split-plot design data, Part 2 (15 minutes)
R tutorial, Module 07
(42 minutes)
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17
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Analysis of covariance
(Module 8)
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Analysis of
covariance, part 1 (13 minutes)
Analysis of
covariance, part 1 (26 minutes)
R tutorial, Module08 (23 minutes)
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18
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Random coefficient models (Module
9)
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Random coefficient models (44 minutes)
R tutorial, Module09 (38 minutes)
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19
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Mixed model
theory, part II (Module 10)
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Mixed model
theory, part II (45 minutes)
R Tutorial,
Module10 (20 minutes)
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20
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Repeated
measures, simple and advanced methods.
(Modules 11 and
12)
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Repeated
measures, simple methods, Part 1 (17 minutes)
Repeated
measures, simple methods, Part 1 (11 minutes)(Nov 2011)
Repeated
measures, simple methods, Part 2 (21 minutes) (NO Audio!)
Repeated
measures, advanced methods, 57 minutes)
R tutorial, module 11 and 12, Part 1 (13 minutes)
R tutorial, module 11 and 12,
Part 2 (13 minutes)
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| 21 |
Introduction to General Linear
Models and link to likelihood theory (02418 lectures based on Madsen
& Thyregod, Chapter 3)
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Part 1: The
General Linear Model, intro (20 minutes)
Part
2: The General Linear Model, definition and likelihood (31 minutes)
Part
3: Hypothesis testing, Type I and III ANOVA tables, post hoc inference
(45 minutes)
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