Basic R and an introductory activity.
Data basics, one factor variable, two factor variables, one numerical variable (numerical analysis only).
One numerical variable (graphical approach). Describing shape of the distribution of a numerical variable.
One factor and one numerical variable, mean/SD vs. median/IQR, the 68-95 Rule and z-scores.
Conditional distribtutions, detecting and describing realtionships between two factor variables.
Inference for the relationship between two factor variables. The Chi-Square test, and simulation of P-values.
Two Numerical Variables: Scatterplots.
Two Numerical Variables: Correlation and Regression.
Two Numerical Variables: Further Considerations.
Sampling: The Idea of random Sampling; Simple Random Sampling, Stratified Sampling, and Cluster Sampling.
Sampling: Bias in Sampling; Selection Bias, Non-Response Bias, and Response Bias.
Design of Studies: Observational studies and Experiments, Completely Randomized Designs.
Design of Studies: Randomized Block Designs, Matched Pairs/Repeated Measures, Further Ideas.
Basic Probability: Discrete Random Variables; Probability Distributions
Basic Probability: Expected Value and Standard Deviation; Binomial Random Variables
Basic Probability: Continuous Random Variables; Approximating Binomials with Normals
Probability in Sampling: Five Basic Parameters, and the Estimators for These Parameters
Probability in Sampling: Defining Parameters for Experiments
Probability in Sampling: The Central Limit Theorem, Probability Applications, the 68-95 Rule for Random Variables
Probability in Sampling: Standard Errors; the 68-95 Rule for Estimation
Confidence Intervals for Means
How Confidence Intervals Work
Confidence Intervals for Proportions
Cautions About Confidence Intervals
Tests of Significance: Tests Involving Means
Tests of Significance: The Relationship Between Tests and Confidence Intervals
Tests of Significance: The Importance of Safety Checks, and Types of Errors
Tests of Significance: Tests Involving Proportions
Tests of Significance: The Dangers of Limited Reporting, and Data Snooping
Chi-Square Test for Goodness of Fit: The Gambler's Die and Data Snooping
Chi-Square Test for Goodness of Fit: Randomness, and "Too-Good-To-Be-True" Tests