Instructor: Yi Yang
Lectures: Monday, Wednesday, Friday; 9:35 AM - 10:25 AM, Stewart Biology Building S1/4
Office hours: Monday, Wednesday; 10:40 AM – 11:40 AM, Burnside Hall 1241
TA: Xiaonan Da xiaonan.da@mail.mcgill.ca
Tutorial Session: Tu/Thur, 8:35am–9:25am, RPHYS 118
Office hours of TA: Tu/Thur, 9:35am–10:35am at BURN 1036
Statistics, 13th edition
James T. McClave & Terry Sincich.
Roughly Chapters 1–9.
5 assignments, only an electronic version is accepted. Upload homework through MyCourses.
| Date | Lecture | Topics | Notes | Reading Assignments |
| Jan 06 | Lecture 1 | Summation notation | Part I 1-6 | Ch 2.3 |
| Jan 08 | Lecture 2 | Measure of central tendency | Part I 7-19 | Ch 2.3 |
| Jan 13 | Lecture 3 | Measure of variability | Part I 20-26 | Ch 2.4 |
| Jan 15 | Lecture 4 | Describing qualitative data | Part I 27-34 | Ch 1.4, 2.1-2.2 |
| Jan 17 | Lecture 5 | Describing quantitative data | Part I 35-47 | Ch 2.5 |
| Jan 10 | Lecture 6 | Cancelled | ||
| Jan 20 | Lecture 7 | Measures of relative standing | Part I 48-57 | Ch 2.6 |
| Jan 22 | Lecture 8 | Detecting outliers | Part I 58-66 | Ch 2.7 |
| Jan 24 | Lecture 9 | Cancelled | ||
| Jan 27 | Lecture 10 | Basic set theory | Part II 1-6 | Ch 3.1 |
| Jan 29 | Lecture 11 | Properties of set operations | Part II 7-9 | Ch 3.1 |
| Jan 31 | Lecture 12 | Sample space and events | Part II 10-15 | Ch 3.2 |
| Feb 03 | Lecture 13 | Sample space and events | Part II 16-22 | Ch 3.3 |
| Feb 05 | Lecture 14 | Naive definition of probability | Part II 23-30 | Ch 3.4 |
| Feb 07 | Lecture 15 | Probability rules | Part II 31-37 | Ch 3.4 |
| Feb 10 | Lecture 16 | Multiplicative rules | Part II 38-44 | Ch 3.6 |
| Feb 12 | Lecture 17 | SWR and SWOR | Part II 45-52 | Ch 3.7 |
| Feb 14 | Lecture 18 | Birthday problems | Part II 53-65 | Ch 3.7 |
| Feb 17 | Lecture 19 | Adjusting for overcounting | Part II 66-69 | Ch 3.7 |
| Feb 19 | Lecture 20 | Binomial coefficient | Part II 70-76 | Ch 3.7 |
| Feb 21 | Lecture 21 | More counting examples | Part II 77-84 | Ch 3.7 |
| Feb 24 | Lecture 22 | Non-naive definition of probability | Part II 85-93 | Ch 3.5 |
| Feb 26 | Lecture 23 | Independence | Part II 94-97 | Ch 3.6 |
| Feb 28 | Lecture 24 | Conditional probability | Part II 98-104 | Ch 3.8 |
| Mar 02 | Lecture 25 | Bayes's rule | Part II 105-119 | Ch 3.9 |
| Mar 30 | Lecture 26 | Random variables | Part III 1-7 | Ch 4.1 |
| Apr 01 | Lecture 27 | Discrete random variables | Part III 8-18 | Ch 4.2 |
| Apr 03 | Lecture 28 | Probability mass functions | Part III 19-26 | Ch 4.2 |
| Apr 06 | Lecture 29 | Bernoulli random variables | Part III 27-32 | Ch 4.4 |
| Apr 08 | Lecture 30 | Binomial random variables | Part III 33- | Ch 4.4 |
| Date | Topics | Code | Data |
| Week 1 | R basics 1 | R code | JamesTut1 |
| Week 2 | R basics 2 | R code | JamesTut2 |
| Week 3 | Basic summary statistics | R code | |
| Week 4 | Sampling using R | R code | |
| Week 5 | Probability rules |
Midterm: 55 minutes, in class, Monday, March 9, 2020
A calculator is allowed, a letter-size cheatsheet, two-sided
Final: 3 hours
A calculator is allowed, a letter-size cheatsheet, two-sided