DSCI 552: Statistical Inference and Computation I
The statistical and probabilistic foundations of inference, the frequentist paradigm. Concepts first introduced through simulation, followed with an introduction and exploration of asymptotic theory.
Sample lecture:
Click on the Binder launch button below to view a sample lecture in an interactive notebook:
Learning Outcomes
By the end of the course, students are expected to be able to:
- Describe the frequentist paradigm of statistical inference.
- Select and compute appropriate estimates of statistical parameters and distributional quantities using bootstrapping and asymptotic methods.
- Construct and run simulations to evaluate uncertainty of estimates and predictions in hypothetical situations.
- Evaluate uncertainty (e.g., confidence intervals) of estimates and predictions from data using bootstrapping and asymptotic results.
- Explain the difference between prediction and estimation error in terms of irreducible and reducible error.
- Carry out basic hypothesis tests in a responsible manner, and responsibly apply them in appropriate situations.
- Explain the fundamental ideas and terminology of hypothesis testing.
- Perform estimation using Maximum Likelihood.
Assessments
This is an assignment-based course. You'll be evaluated as follows:
Assessment | Weight | Deadline | Location |
---|---|---|---|
Lab 1 - Understanding sampling through simulation and bootstrapping | 20% | 2018-10-20 @ 18:00 | Submit to Github |
Lab 2 - Confidence Intervals | 15% | 2018-10-27 @ 18:00 | Submit to Github |
Lab 3 - Hypothesis Testing | 15% | 2018-11-10 @ 18:00 | Submit to Github |
Lab 4 - Asympototic theory and Maximum Likelihood estimation | 10% | 2018-11-06 @ 18:00 | Submit to Github |
Quiz 1 | 20% | 2018-10-29 @ 14:00 | On Canvas and written in your lab room |
Quiz 2 | 20% | 2018-11-08 @ TBD | On Canvas and written in TBD |
Tip: Use the lecture learning objectives as beacons when studying for your quizzes!
Schedule
Lecture | Topic | Readings |
---|---|---|
1 | Understanding sampling through simulation | |
2 | Bootstrapping | |
3 | Confidence intervals using bootstrapping | |
4 | Introduction to Hypothesis testing | |
5 | There is only one test | |
6 | An introduction to Asympototic theory | |
7 | Using Asympototic theory for confidence intervals and hypothesis tests | |
8 | Maximum Likelihood Estimation |
Textbooks
- Modern Dive: An Introduction to Statistical and Data Sciences by R by Chester Ismay and Albert Y. Kim
- OpenIntro Statistics by David M Diez, Christopher D Barr and Mine Cetinkaya-Rundel