Announcements! ( See All )
2/22 - HW 5 is due on Tuesday 02/23 at 11:59 PM; HW 6 is due next Monday March 03/01 at 11:29 PM

Week 1

Getting Started

Week 2

Conditioning

Week 3

Random Variables

Week 4

Random Variables and Expectation

Week 5

The Power of Additivity

Week 6

Path to Prediction

Week 7

Midterm Week

Week 8

Variability

Week 9

Large Random Samples

Week 10

Spring Recess

Week 11

Fundamentals of Inference

Week 12

Continuous Distributions

Week 13

Bias and Variance

Week 14

The Error in a Regression Estimate

Week 15

Inference in Regression

WeekDateContentNotes & Assignments
1 Mon 01/18 No school
Tues 01/19 No school
Wed 01/20 Lecture 1: Course introduction and the basics (Sections 1.1, 1.2) Lecture 1 Pre-lecture Notes
Lecture 1 Notes
Lecture 1 Video
HW 1 (Due Fri 01/29 at 11:59 PM)
Thu 01/21 Discussion
Fri 01/22 Lecture 2: Axioms of Probability (Section 1.3) Lecture 2 Pre-lecture Notes
Lecture 2 Notes
Lecture 2 Video
2 Mon 01/25 Lecture 3: Axioms of Probability, Intersections (section 1.3, 2.1) Lecture 3 Pre-lecture Notes
Lecture 3 Notes
Lecture 3 Video
HW 2 (Due Mon 02/01 at 11:59 PM)
Tues 01/26 Discussion
Wed 01/27 Lecture 4: Symmetry in Sampling, Bayes’ Rule (section 2.2, 2.3) Lecture 4 Pre-lecture Notes
Lecture 4 Notes
Lecture 4 Kahoot
Lecture 4 Video
Thu 01/28 Discussion
Fri 01/29 Lecture 5: Bayes’ Rule: definition, use, and interpretation, Independence (section 2.2, 2.3, 2.5) Lecture 5 Pre-lecture Notes
Lecture 5 Notes
Lecture 5 Video
Exam-prep Worksheet 1
3 Mon 02/01 Lecture 6: Finish up chapter 2, Random variables & their distributions (section 2.4, 3.1, 3.2) Lecture 6 Pre-lecture Notes
Lecture 6 Notes
Lecture 6 Video
HW 3 (Due Mon 02/08 at 11:59 PM)
Tues 02/02 Discussion
Wed 02/03 Lecture 7: Random variables & their distributions + 2 special distributions (section 3.1, 3.2, 3.3, 3.4) Lecture 7 Pre-lecture Notes
Lecture 7 Notes
Lecture 7 Jupyter Notebook
Lecture 7 Video
Thu 02/04 Discussion
Fri 02/05 Lecture 8: The hypergeometric distribution, examples, CDF (section 3.4, 3.5, 4.1) Lecture 8 Pre-lecture Notes
Lecture 8 Notes
Lecture 8 Video
4 Mon 02/08 Lecture 9: CDF, Waiting Times (section 3.5, 4.1, 4.2) Lecture 9 Pre-lecture Notes
Lecture 9 Notes
Lecture 9 Video
HW 4 (Due Wed 02/17 at 11:59 PM)
Tues 02/09 Discussion
Wed 02/10 Lecture 10: Waiting Times, Exponential Approximations (section 4.2, 4.3) Lecture 10 Pre-lecture Notes
Lecture 10 Notes
Lecture 10 Video
Thu 02/11 Discussion
Fri 02/12 Lecture 11: Waiting times, exponential approximations, the Poisson distribution (section 4.3, 4.4) Lecture 11 Pre-lecture Notes
Lecture 11 Notes
Lecture 11 Video
5 Mon 02/15 No school
Tues 02/16 Discussion
Wed 02/17 Lecture 12: Sums of Poisson RVs, Expectation, Functions of RVs (section 4.4, 5.1, 5.2) Lecture 12 Pre-lecture Notes
Lecture 12 Notes
Lecture 12 Video
HW 5 (Due Tue 02/23 at 11:59 PM)
Thu 02/18 Discussion
Fri 02/19 Lecture 13: Functions of RVs, Method of indicators (section 5.2, 5.3) Lecture 13 Pre-lecture Notes
Lecture 13 Notes
Lecture 13 Video
6 Mon 02/22 Lecture 14: Method of indicators, definition of unbiased estimators (section 5.3, 5.4) Lecture 14 Pre-lecture Notes
Lecture 14 Notes
Lecture 14 Video
HW 6 (Due Mon 03/01 at 11:59 PM)
Tues 02/23 Discussion
Wed 02/24 Lecture 15: Unbiased estimators, Conditional expectation (section 5.4, 5.5) Lecture 15 Pre-lecture Notes
Lecture 15 Notes
Lecture 15 Video
Thu 02/25 Discussion
Fri 02/26 Lecture 16: Expectation by conditioning (section 5.5) Lecture 16 Pre-lecture Notes
Lecture 16 Notes
Lecture 16 Video
7 Mon 03/01 Lecture 17: Examples: putting things together
Tues 03/02 Discussion
Wed 03/03 Midterm review
Thu 03/04 Discussion
Fri 03/05 Midterm
8 Mon 03/08 Lecture 20: Measuring variability (section 6.1, 6.2)
Tues 03/09 Discussion
Wed 03/10 Lecture 21: Typical or unusual? Tails of distributions (section 6.3, 6.4)
Thu 03/11 Discussion
Fri 03/12 Lecture 22: Variability of a random sample sum (section 7.1)
9 Mon 03/15 Lecture 23: The accuracy of a simple random sample (section 7.2)
Tues 03/16 Discussion
Wed 03/17 Lecture 24: The law of averages (section 7.3)
Thu 03/18 Discussion
Fri 03/19 Lecture 25: Central Limit Theorem (section 8.1, 8.2)
10 Mon 03/22 No school
Tues 03/23 No school
Wed 03/24 No school
Thu 03/25 No school
Fri 03/26 No school
11 Mon 03/29 Lecture 26: Normal approximations (section 8.3, 8.4)
Tues 03/30 Discussion
Wed 03/31 Lecture 27: Inference: testing hypotheses (section 9.1)
Thu 04/01 Discussion
Fri 04/02 Lecture 28: Inference: testing hypotheses (section 9.2) Continued
12 Mon 04/05 Lecture 29: Confidence intervals continue (section 9.3, 9.4)
Tues 04/06 Discussion
Wed 04/07 Lecture 30: Probability density (section 10.1, 10.2)
Thu 04/08 Discussion
Fri 04/09 Lecture 31: The exponential distribution (section 10.3)
13 Mon 04/12 Lecture 32: Some properties of the normal distribution (section 10.4)
Tues 04/13 Discussion
Wed 04/14 Lecture 33: Prediction: bias-variance (section 11.1)
Thu 04/15 Discussion
Fri 04/16 Lecture 34: The bias-variance tradeoff (section 11.2)
14 Mon 04/19 Lecture 35: Correlation (section 11.3, 11.4)
Tues 04/20 Discussion
Wed 04/21 Lecture 36: The regression estimates, effect and fallacy (section 11.4, 11.5)
Thu 04/22 Discussion
Fri 04/23 Lecture 37: The regression model for data (section 12.1)
15 Mon 04/26 Lecture 38: Inference for the true slope in the regression model (section 12.2, section 12.3)
Tues 04/27 Discussion
Wed 04/28 Lecture 39: Conclusion
Thu 04/29 Discussion
Fri 04/30 Lecture 40: Review