Graphical displays and numerical summaries, data collection methods, probability, sampling distributions, confidence intervals and hypothesis testing involving one or two means and proportions, contingency tables, correlation and simple linear regression.
Course Outline: 
Lesson 1: Course Introduction, Syllabus, and Learning Strategies
Lesson 2: The Big Picture in Statistics
Lesson 3: Producing Data–Sampling
Lesson 4: Cautions in Sample Surveys
Lesson 5: Producing Data–Experiments
Lesson 6: Design of Experiments
Lesson 7: Examining Distributions of Quantitative Variables with Graphs
Lesson 8: Examining Distributions with Numerical Measures, Part 1
Lesson 9: Examining Distributions with Numerical Measures, Part 2
Lesson 10: Introduction to Probability
Lesson 11: Random Variables and Probability Distributions
Lesson 12: Normal Probability Distributions and Standard Scores
Lesson 13: The Standard Normal Distribution and Its Applications
Lesson 14: Sampling Distribution of X-Bar and the Central Limit Theorem
Lesson 15: Calculating Probabilities Associated with X-Bar
Lesson 16: Statistical Process Control
Lesson 17: Introduction to Inference
Lesson 18: One-sample t Confidence Interval for Means
Lesson 19: Margin of Error and Sample Size Calculations
Lesson 20: Overview of Hypothesis Testing
Lesson 21: One-sample t-Test for Means
Lesson 22: Hypothesis Testing and Confidence Intervals
Lesson 23: Error Probabilities and Power of a Test–Cautions in Inference
Lesson 24: EDA for Categorical Variables and Sampling Distribution of P-Hat
Lesson 25: One-Sample Z-Confidence Interval for Proportions
Lesson 26: One-Sample Z-Test for Proportions
Lesson 27: Role-Type Classifications; EDA for C to Q Data
Lesson 28: Matched Pairs t Procedures
Lesson 29: Two-sample t Procedures for Means
Lesson 30: Analysis of Variance (ANOVA)
Lesson 31: Two-Way Tables and Conditional Distributions
Lesson 32: Two-sample z-Procedures for Proportions
Lesson 33: Chi-square Test of Independence
Lesson 34: Scatterplots and Correlation
Lesson 35: Linear Regression and r-squared
Lesson 36: Cautions in Correlation and Regression Analysis
Lesson 37: Inference for Slope of Regression Line
Lesson 38: Inference for Regression Predictions: CI and PI