## page was renamed from Synopsis2007
= Synopsis of the CBU Graduate Statistics Course 2008 =

 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'''

  * The Naming of Parts
  * Categorical Data
  * Frequency Tables
  * The Chi-Squared Goodness-of-Fit Test
  * The Chi-squared Distribution
  * The Binomial Test
  * The Chi-squared test for association
  * Simpson, Cohen and McNemar
  * SPSS procedures that help
   * Frequencies
   * Crosstabs
   * Chi-square
   * Binomial
  * Types of Data
   * Quantitative
   * Qualitative
   * Nominal
   * Ordinal
  * Frequency Table
  * Bar chart
  * Cross-classification or Contingency Table
  * Simple use of SPSS Crosstabs
  * Goodness of Fit Chi-squared Test
  * Chance performance and the Binomial Test
  * Confidence Intervals for Binomial Proportions
  * Pearson’s Chi-squared
  * Yates’ Continuity Correction
  * Fisher’s Exact Test
  * Odds and Odds Ratios
  * Log Odds and Log Odds ratios
  * Sensitivity and Specificity
  * Signal Detection Theory
  * Simpson’s Paradox
  * Measures of agreement: Cohen's Kappa
  * Measures of change: McNemar’s Test
  * Association or Independence: Chi-squared test of association
  * Comparing two or more classified samples

 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'''

  * GLM and Simple Linear Regression
  * The Design Matrix
  * Least Squares
  * ANOVA and GLM
  * Types of Sums of Squares
  * Multiple Regression as GLM
  * Multiple Regression as a sequence of GLMs in SPSS
  * The two Groups t-test as a GLM
  * One-way ANOVA as GLM
  * Multi-factor Model
   * Additive (no interaction)
   * Non-additive (interaction)
  * Analysis of Covariance
   * Simple regression
    * 1 intercept
    * 1 slope
   * Parallel regressions
    * multiple intercepts
    * 1 slope
   * Non-parallel regressions
    * multiple intercepts
    * multiple slopes
  * Sequences of GLMs in ANCOVA


 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'''

  * Two sample t-Test vs. Paired t-Test
  * Repeated Measures as an extension of paired measures
  * Single factor Within-Subject design
  * Sphericity
  * Two (or more) factors Within-Subject design
  * Mixed designs combining Within- and Between-Subject factors
  * Mixed Models, e.g. both Subjects & Items as Random Effects factors
  * The ‘Language as Fixed Effects’ Controversy
  * Testing for Normality
  * Single degree of freedom approach

 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. '''What to do following an ANOVA'''

  * Why do we use follow-up tests?
  * Different ways to follow up an ANOVA
  * Planned vs. Post Hoc Tests
  * Choosing and Coding Contrasts
  * Handling Interactions
  * Standard Errors of Differences
  * Multiple t-tests
  * Post Hoc Tests
  * Trend Analysis
  * Unpacking interactions
  * Multiple Comparisons: Watch your Error Rate!
  * Post-Hoc vs A Priori Hypotheses
  * Comparisons and Contrasts
  * Family-wise (FW) error rate
  * Experimentwise error rate
  * Orthogonal Contrasts or Comparisons
  * Planned Comparisons vs. Post Hoc Comparisons
  * Orthogonal Contrasts/Comparisons
  * Planned Comparisons or Contrasts
  * Contrasts in GLM
  * Post Hoc Tests
  * Control of False Discovery Rate (FDR)
  * Simple Main Effects