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    * *Permissions*
    * How to cite these pages
    * Introductory remarks and an advisory
    * Is statistics hard?
    * An all-important foundation!
          o Cause & Effect
          o Study design
                + Intention-To-Treat Analysis
                + Meta Analysis
          o Random Samples / Randomization
          o Randomization plans
          o Units of analysis
          o Creating data files
    * The basics
          o Look At the Data!
          o Logarithms
          o Summary Statistics
                + Location and Spread
                + Correlation Coefficients
          o Probability
          o The Normal Distribution
          o Outliers
          o The Behavior of Sample Means
            (or Why Confidence Intervals Always Seem to be Based On the Normal Distribution)
    * Confidence intervals and tests of signficance
          o Confidence Intervals
                  Probability & Statistics / A Boy & His Dog
                  CIs for Logarithmically Transformed Data
                  LARGE SAMPLE Formulas for Confidence Intervals Involving Population Means Paired Data / Paired Analyses
          o
                  What does pairing really do? The Ubiquitous Sample Mean!
        o o What Student Did
          o What Student Really Did
          o Significance Tests
                + Prologue
                + Significance Tests/Hypothesis Testing
                + Significance Tests Simplified
                + Student's t Test for Independent Samples
                + P values
                + Why P=0.05?
                + A Valuable Lesson
                + One-Sided Tests
          o Contingency Tables
          o Proportions
          o Odds
          o Paired Counts
    * Sample Size Calculations
          o Some Underlying Theory & Some Practical Advice
                + An Underappreciated Consequence of Sample Size Calculations As They Are Usually Performed
          o Controlled Trials
          o Surveys
          o Group Randomized, Multi-level, and Hierarchical Studies
    * Nonparametric Statistics
    * Simple Linear Regression
          o Introduction to Simple Linear Regression
          o How to Read the Output From Simple Linear Regression Analyses
          o Correlation and Regression
          o Frank Anscombe's Regression Examples
          o Transformations In Linear Regression
          o Which fit is better?
          o The Regression Effect / The Regression Fallacy
    * Comparing Two Measurement Devices: Part I
    * Comparing Two Measurement Devices: Part II
    * Linear models: Nomenclature
    * Multiple Linear Regression
          o Introduction to Regression Models
          o Student's t Test for Independent Samples Is A Special Case of Simple Linear Regression
          o Introduction to Multiple Linear Regression
          o The Most Important Lesson You'll Ever Learn About Multiple Linear Regression Analyses
          o How to Read the Output From Multiple Linear Regression Analyses
          o The Meaning of Regression Coefficents
          o What Does Multiple Regression Look Like?
          o What Does Multiple Regression Look Like? (Part 2)
          o Why Is a Regression Line Straight?
          o Partial Correlation Coefficients
          o Which Predictors Are More Important?
          o The Extra Sum of Squares Principle
          o Simplifying A Multiple Regression Equation
                + Using the Bootstrap to Simplify a Multiple Regression Equation
                + The Real Problem!
          o Which variables go into a multiple regression equation?
          o The Mechanics of Categorical Variables With More Than Two Categories
          o Interactions In Multiple Regression Models
          o Regression Diagnostics
                + Collinearity
                        Centering Other Regression Diagnostics
    +
    * Analysis of Variance
          o Single Factor ANOVA
          o How to Read the Output From One-Way Analysis of Variance
          o Multiple Comparisons
          o Labeling Similar Means After Performing an Analysis of Variance
                + A web page that creates the labels
          o Adjusting Results for Other Variables
                + Adjusted Means, a.k.a. Least Squares Means
                + Adjusted Means: Adjusting For Numerical Variables
                + Adjusted Means: Adjusting For Categorical Variables
                + Which Variables Should We Adjust For?
          o Multi-Factor Analysis of Variance
          o The Model For Two-Factor Analysis of Variance
          o Pooling Effects
          o Fixed and Random Factors
                  Randomized (Complete) Block Designs Repeated measures analysis of variance
          o
                + Repeated measures analysis of variance: Part I
                + Repeated measures analysis of variance: Part II
                      # Why SAS's PROC MIXED Can Seem So Confusing
                + Pre-test/Post-test Experiments
                + Serial Measurements
    * Crossover Studies

# Logistic Regression
# Poisson Regression
# Degrees of Freedom

Read fulltext:  The Little Handbook of Statistical Practice