Course Schedule
MATH 3080 - Spring 2024
Below is a course schedule. It contains an outline of course topics, reading assignments, homework, quiz and exam dates. By default, the assigned readings will be from the textbook Mathematical Statistics with Applications in R (see Syllabus). From time to time there will be links to supplementary notes or material.
Note: This schedule is approximate and will evolve as the course progresses. Lectures may be rearranged and split between days depending on course progress. The exams dates are tentative and will be finalized at least a couple weeks before the date.
| Week | Date | Topics | Section | HW |
|---|---|---|---|---|
| 1 | Sampling Distributions | Ch 4 | ||
| F 1/19 | Introduction, Review of Prelims, Random Variables, Limit Theorems | |||
| 2 | M 1/22 | Intro to Sampling, Normal Distributions, Chi Squared | 4.1, 4.2.1 | |
| W 1/24 | Normal Distributions, Student t-distribution, F-distribtution | 4.2.2, 4.2.3 | ||
| F 1/26 | R Lab | |||
| 3 | M 1/29 | Order Statistics | 4.3 | |
| W 1/31 | Normal Approximation | 4.4 | ||
| F 2/02 | R Lab | |||
| 4 | Statistical Estimation | Ch 5 | ||
| M 2/05 | Into to Estimators, Point Estimators, Method of Moments | 5.1, 5.2.1 | HW 1 Due | |
| W 2/07 | Maximum Likelihood Estimators | 5.2.2 | ||
| F 2/09 | Mardi Gras Break 🎉 - No Class | |||
| 5 | M 2/12 | |||
| W 2/14 | Properties of Estimators | 5.3 | ||
| F 2/16 | Properties of Estimators | 5.3 | ||
| 6 | M 2/19 | Error Estimation and confidence intervals - pivotal method | 5.4 | HW 2 Due |
| W 2/21 | Single Population intervals | 5.5 | ||
| F 2/23 | Variance intervals and two populations | 5.6, 5.7 | ||
| 7 | Hypothesis Testing | Ch 6 | ||
| M 2/26 | Intro/ Definitions | 6.1 | HW 3 Due | |
| W 2/28 | Neyman Pearson Lemma | 6.2 | ||
| F 3/01 | Midterm Exam I | Ch 4 - 5 | ||
| 8 | M 3/04 | Likelihood Ratio Tests | 6.3 | |
| W 3/06 | Single Parameter Tests | 6.4 | ||
| F 3/08 | R Lab | |||
| 9 | M 3/11 | Two Sample Tests | 6.5 | HW 4 Due |
| W 3/13 | Two Sample Tests cont/ Buffer Day | 6.5 | ||
| F 3/12 | R Lab | |||
| 10 | Linear Regression | Ch 7 | ||
| M 3/18 | Intro, Basic Linear Models | 7.1 | HW 5 Due | |
| W 3/20 | Basic Linear Models | 7.2 | ||
| F 3/22 | R Lab | |||
| M 3/25 | Spring Break 🌴 - No Class | |||
| W 3/27 | ||||
| F 3/29 | ||||
| 11 | M 4/01 | Inference on Least Square Estimators | 7.3 | |
| W 4/03 | Predicting a particular value | 7.4 | ||
| F 4/05 | R Lab | |||
| 12 | M 4/08 | Correlation Analysis | 7.5 | HW 6 Due |
| W 4/10 | Multiple regression and matrices | 7.6 | ||
| F 4/12 | R Lab | |||
| 13 | Bayesian Inference | Ch 10 | ||
| M 4/15 | Intro | 10.1 | ||
| W 4/14 | Bayesian Point Estimation | 10.2 | ||
| F 4/19 | Midterm Exam II | Ch 6 - 7 | ||
| 14 | M 4/22 | Bayesian Point Estimation | 10.2 | |
| W 4/24 | Bayesian confidence interval | 10.3 | ||
| F 4/26 | Bayesian hypothesis testing | 10.4 | ||
| 15 | M 4/29 | Bayesian decision theory | 10.5 | HW 7 Due |
| W 5/01 | Empirical Bayes Estimates | 10.6 | ||
- 2024