TOC
* Introductory remarks and an advisory
* Is statistics hard?
* An all-important foundation!
o Cause & Effect
o Study design
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
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
o Paired Data / Paired Analyses
o What Student Did
o What Student Really Did
o Significance Tests
o Contingency Tables
o Proportions
o Odds
o Paired Counts
* Sample Size Calculations
o Some Underlying Theory & Some Practical Advice
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
* 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
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
* Crossover Studies
# Logistic Regression
# Poisson Regression
# Degrees of Freedom
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