Instructor: Dr. Adam TIMAR
Text:
Freedman, D. A. (2009): Statistical Models: Theory and Practice, Cambridge: Cambridge University Press, revised edition, with a foreword by David Collier, Jasjeet Singh Sekhon and Philip B. Stark.
Prerequisite: Introductory linear algebra and probability theory. Depending on the background of the students we may go through the background material that we need.
Course description
Statistics is one of the most important areas of applied mathematics, which is widely used and misused.
This class will provide an introduction to some of the basic techniques, with a special emphasis on examples.
- Does smoking cause lung cancer? Or do the same people choose to smoke as the ones who have a higher risk of lung cancer?
- Are you more likely to get obese if the friends of your friends get obese?
- In a large-scale experiment, those women who rejected mammography screening had a lower rate of breast cancer than those who did not. Does it imply that mammography increases the chance of breast cancer?
Topics:- Introduction
- Review of the basics of probability
- The regression line, simple regression
- Multiple regression
- Explained variance
- Collinearity
- Association or causation?
- Best Linear Unbiased Estimator
- Statistical significance
- The F-test
- Path models
- Maximum likelihood
- Probit models
- Logit models
- Interesting examples and applications…