PHP2601
Fall2014
'
$
1. Introduction
1.1 General Linear Model
1.1.1 Motivating examples
Primary objective: to assess the relationship between continuous
response and continuous and/or discrete explanatory variables (predictors).
The focus is on the mean

PHP2601
Fall2014
'
$
General guidelines for selection of mean and covariance models
Reading
Fitzmaurice, Laird, and Ware
Chapter 5: Modelling the mean: Analyzing response prole
Chapter 6: Modelling the mean: Parametric curves
Chapter 7: Modelling the c

Introduction
Residuals
Inuence Diagnostics
Remark
PHP2603: Analysis of Longitudinal Data
Eunhee Kim
Spring 2013
1 / 45
Introduction
Residuals
Inuence Diagnostics
Remark
Chapter 5. Residual Analysis and Diagnostics
Reading
Fitzmaurice, Laird and Ware Chapt

PHP2601
Fall2014
'
$
Chapter 7. Multivariate Analysis of Variance (MANOVA)
Multivariate analysis of variance (MANOVA) is an extension of the ANOVA
model to handle cases with multiple response variables.
Reading:
Planned Contrasts and Post Hoc Tests in MA

PHP2601
Fall2014
'
$
Chapter 8. General linear models for longitudinal data
Reading
(Required) Davidian, Chapter 8: General linear models for longitudinal data
(Recommended) Fitzmaurice, Laird, and Ware, Sections 3.1-3.5, Sections 7.1-7.5
&
1
%
PHP2601

Planned Contrasts and Post Hoc Tests in MANOVA Made Easy
Chii-Dean Joey Lin, SDSU, San Diego, CA
ABSTRACT
Multivariate analysis of variance (MANOVA) is a multivariate version of analysis of variance (ANOVA). While an
ANOVA is considered to test if there i

PHP2601: Linear Models, Fall 2014
Assessing Multivariate Normality
Reading: Johnson and Wichern (JW) Chapters 4.6, 4.7, 4.8
1. Assessing Multivariate Normality
The methods for assessing multivariate normality of a set of data make
use of the properties of

ST4233, Linear Models, Semester 1 2008-2009
Ch7. Multiple Regression: Tests of Hypothesis and Condence
Intervals
In this chapter we consider hypothesis tests and condence intervals for the parameters
0 , , k in in the model y = X + . We will assume throug

PHP2601
Fall2014
'
$
Chapter 4. Hypotheses Testing
4.1 The General Linear (Univariate) Hypothesis, GLH
For testing, we assume i.i.d. Gaussian errors.
is the matrix of primary parameters, and a1 = Cap p1 is a matrix of
secondary parameters. Cap (a p) is t

PHP2601
Kim
'
$
Chapter 6. Analysis of Variance (ANOVA)
Motivation:
The study of ANOVA is motivated by desire to model and test hypotheses
about two or more group means.
We use analysis of variance (ANOVA) to answer questions like the following.
Do two o

PHP2601
Fall2014
'
$
Chapter 3. GLM Estimation
Reading Assignment: Seber & Lee, pp38-50.
Review: General Linear Model (GLM)
We will consider the case in which we observe a single response and one or more
covariates.
We write the general linear model
y n1

PHP2601
Fall2014
'
$
Chapter 2. Vectors of Random Variables
Reading:
Seber & Lee: Chapter 1 and Chapter 2
&
1
%
PHP2601
Fall2014
'
$
2.1 Random Vectors and Matrices
Denitions 2.1
1. A random vector is a vector of random variables
Y
1
.
Y = .
.
Yn