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1. Lecture 1: Collecting Data
2. Lecture 2: The R Project for Statistical Computing
3. Lecture 3: Distribution of Quantitative Variables
4. Lecture 4: Variables and Functions in R
5. Lecture 6: Body Mass Index, Numeric Summaries of Quantitative Data
6. Lecture 7: Graphics Composition Overview
7. Lecture 8: Scatter Plots
8. Lecture 8: Time
9. Lecture 9: Numerical Summaries of Data
10. Lecture 10: Fisher's "Lady Tasting Tea" Experiment
11. Lecture 11: Designs of Experiments
12. Lecture 12: Design of Experiments
13. Lecture 13: The Box Model
14. Lecture 14: Informal Analysis of an Experiment
15. Lecture 15: Deck of Cards
16. Lecture 16: Conditional Probability
17. Lecture 17: Pascal's & Fermat's Solutions
18. Lecture 18: R and Probability
19. Lecture 19: Hypothesis Testing
20. Lecture 20: Roulette Wheel
21. Lecture 21: 0-1 Boxes and Hypothesis Tests
22. Lecture 22: Two Probability Laws and Hypothesis Tests for Averages
23. Lecture 23: Central Limit Theorem
24. Lecture 24: Hypothesis Tests for Averages
25. Lecture 25: Survey Design
26. Lecture 26: Project Overview
27. Lecture 27: Sampling
28. Lecture 28: Featured Plots
29. Lecture 29: More on Sampling
30. Lecture 30: Correlation and Regression
31. Lecture 31: Fitting Lines to Data
32. Lecture 33: More on Regression
33. Lecture 34: Regression Concepts
34. Lecture 35: Emails
35. Lecture 36

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