STAT 200 - Introductory Statistics (Winter 2026)

Course Information

• Syllabus
• Tentative Schedule

Lecture Slides

• Lecture #1: An Introduction to Statistics (PDF Version).
• Lecture #2: Sampling Theory (PDF Version).
• Lecture #3: Types of Variables & Conducting a Statistical Study (PDF Version).
• Lecture #4: What Not to Do: Types and Sources of Bias (PDF Version).
• Lecture #5: Graphics for Categorical Variables (PDF Version).
• Lecture #6: Graphics for Numeric Variables (PDF Version).
• Lecture #7: Measures of Center and of Position (PDF Version).
• Lecture #8: Measures of Spread (PDF Version).
• Lecture #9: Correlation & Linear Regression (PDF Version).
• Lecture #10: The Basics of Probability (PDF Version).
• Lecture #11: Discrete Random Variables (PDF Version).
• Lecture #12: The Bernoulli, Binomial and Poisson Distributions (PDF Version).
• Lecture #13: Continuous Distribution & the Normal Distribution (PDF Version).
• Lecture #14: The Normal Approximation of the Binomial Distribution (PDF Version).
• Lecture #15: The Central Limit Theorem (PDF Version).
• Lecture #16: The Theory of Confidence Intervals (PDF Version).
• Lecture #17: The Practice of Confidence Intervals (PDF Version).
• Lecture #18: The Theory of Hypothesis Tests (PDF Version).
• Lecture #19: The Practice of Hypothesis Tests (PDF Version).
• Lecture #20: Chi-Square Goodness-of-Fit Test (PDF Version).
• Lecture #21: The Analysis of Variance (ANOVA) (PDF Version).
• Lecture #22: Beyond the ANOVA Procedure (PDF Version).
• Lecture #23: Testing Categorical Independence (PDF Version).
• Lecture #24: Covariance and Correlation (PDF Version).
• Lecture #25: Returning to Regression (PDF Version).