## Course description, Outline and Overview

This is a statistics course for postgraduate students in the Faculty of
Agricultural and Environmental Sciences at McGill University. The
orientation of this course is to the application of statistics in the
biometrical field; the use of various statistical methods related to
*Analysis of Variance* in the interpretion of experimental research
data. The SAS statistical package will be used extensively throughout
this course, both to demonstrate how to carry out an analysis and to show
the proper SAS statements.

#### Course description, as given in the McGill Graduate Programme Book

Principles of linear models, multiple regression equations and classification
models. Introduction to Analysis of Variance and common statistical designs
used in agricultural and environmental sciences. Emphasis on balanced and
unbalanced designs and data structures; their analysis and tests of
statistical significance.

Prerequisite: AEMA 310 Statistical Methods I, or an equivalent
undergraduate statistics course.

#### Course outline and topics to be covered

##### Quantitiative traits, what they are and are not, examples

##### A brief introduction to matrices

##### Review of simple linear regression (from Statistical Methods I)

##### Multiple Regression

##### ..Assumptions of the model

##### ..Linear model in statistical, biological and biometrical terms

##### Least Squares

##### ..Derivation of Least Squares, how we get the equations that we use

##### ..Obtaining a solution

##### ..Estimated model equations

##### ..Parameter estimates and standard errors

##### ..Sums of Squares and ANOVA

##### t-test and confidence intervals

##### Linear and quadratic regressions and interactions

##### Curve fitting

##### Correlations, when is a correlation appropriate?

##### ..Partial correlations

##### ..Confidence interval and tests of significance

##### Classification models

##### One-way classification

##### ..Assumptions

##### ..Linear model

##### ..Obtaining a solution

##### ..What is estimable

##### ..Sums of Squares and ANOVA

##### ..Treatment differences

##### ..Multiple comparisons

##### ..Homogeneity of variance and Normality

##### Two-way classification

##### ..Assumptions

##### ..Linear model

##### ..Obtaining a solution

##### ..What is estimable

##### ..Sums of Squares and ANOVA

##### ..Treatment differences

##### Type I and Type III Sums of Squares

##### Gains in efficiency due to use of 'blocks'

##### Subsampling and nested, hierarchical models

##### Factorial models

##### ..What is estimable

##### ..Sums of Squares and ANOVA

##### ..Treatment differences

##### Latin Square models

##### ..What is estimable

##### ..Sums of Squares and ANOVA

##### ..Treatment differences

##### Analysis of Covariance

##### ..What is estimable

##### ..Sums of Squares and ANCOVA

##### ..Treatment differences

##### Split Plot models

##### ..What is estimable

##### ..Sums of Squares and ANOVA

##### ..Treatment differences

##### Fixed effects or Random effects

##### Mixed models

##### ..Assumptions

##### ..Linear model

##### ..Obtaining a solution

##### ..What is estimable

##### ..Sums of Squares and ANOVA

##### ..Treatment differences

R.I. Cue, 2010 May 4