= Synopsis of the Graduate Statistics Course 2007 =

 1. '''The Anatomy of Statistics: Models, Hypotheses, Significance and Power'''

  * Experiments, Data, Models and Parameters

  * Probability vs. Statistics

  * Hypotheses and Inference

  * The Likelihood Function

  * Estimation and Inferences

  * Maximum Likelihood Estimate (MLE)

  * Schools of Statistical Inference

   * Ronald Aylmer FISHER

   * Jergy NEYMAN and Egon PEARSON

   * Rev. Thomas BAYES

  * R A Fisher: P values and Significance Tests

  * Neyman and Pearson: Hypothesis Tests

  * Type I & Type II Errors

  * Size and Power

 1. '''Exploratory Data Analysis (EDA)'''

  * What is it?

  * Skew and kurtosis: definitions and magnitude rules of thumb

  * Pictorial representations - in particular histograms, boxplots and stem and leaf displays

  * Effect of outliers

  * Power transformations

  * Rank transformations

 1. '''Categorical Data Analysis'''

 1. '''Regression'''

  * What is it?

  * Expressing correlations (simple regression) in vector form

  * Scatterplots

  * Assumptions in regression

  * Restriction of range of a correlation

  * Comparing pairs of correlations

  * Multiple regression

  * Least squares

  * Residual plots

  * Stepwise methods

  * Synergy

  * Collinearity

 1. '''Between subjects analysis of variance'''

  * What is it used for?

  * Main effects

  * Interactions

  * Simple effects

  * Plotting effects

  * Implementation in SPSS

  * Effect size

  * Model specification

  * Latin squares

  * Balance

  * Venn diagram depiction of sources of variation

 1. '''The General Linear Model and complex designs including Analysis of Covariance'''

 1. '''Power analysis'''

  * Hypothesis testing

  * Boosting power

  * Effect sizes: definitions, magnitudes

  * Power evaluation methods:description and implementation using an examples  

   * nomogram 

   * power calculators

   * SPSS macros 

   * spreadsheets 

   * power curves 

   * tables

   * quick formula

 1. '''Repeated Measures and Mixed Model ANOVA'''

 1. '''Latent variable modelling – factor analysis and all that!'''

  * Path diagrams – a regression example

  * Comparing correlations

  * Exploratory factor analysis

  * Assumptions of factor analysis

  * Reliability testing (Cronbach’s alpha)

  * Fit criteria in exploratory factor analysis

  * Rotations

  * Interpreting factor loadings

  * Confirmatory factor models

  * Fit criteria in confirmatory factor analysis

  * Equivalence of correlated and uncorrelated models

  * Cross validation as a means of assessing fit for different models

  * Parsimony : determining the most important items in a factor analysis 

 1. '''Post-hoc tests, multiple comparisons, contrasts and handling interactions'''