The objective of this course is to introduce students to the basic statistical techniques that are widely used in business and other fields. In particular, considerable attention will be devoted to the technique of regression analysis, which is a useful and powerful technique for modeling the relationships between variables of interest.
Full syllabus here: pdf
Background material on topics prerequisite to this course, including lecture notes and a variety of textbook references, is available on the course page for Stat-UB.103.
- To be posted.
- Stats review
- Practical issues with tests
- Correlation and covariance
- Coefficients and predictions
- Model inference (Lab.Rmd)
- Multiple regression, other extensions
- Model selection and validation
- Classification with logistic regression
- Common regression pitfalls
- References to learn more
- Principles and ethics
These references are generally good, and some parts of them closely match the material we are covering.
- ModernDive (MD)
- Learning Statistics with R (LSR)
- OpenIntro Statistics (OIS) (has exercises and solutions)
- Introduction to Data Science (IDS)
- Statistics, 4th Edition, by Freedman, Pisani, and Purves (FPP). Textbook, not required.
Specific chapter or section references for various topics are as follows.
- Estimation: LSR 10; IDS 32-33; OIS 4.1-2; FPP 21, 23-24.
- Intervals and hypothesis tests: MD Appendix B; LSR 10.5, 11, 13; OIS 4.2-5, 5.1-4, 6.1-2,4-6; IDS 34, 38; FPP 26-29.
- Covariance and simple regression: MD 6; LSR 5.7, 15.1-2,4,6,8,9; OIS 7; FPP 8-12.
- Multiple regression: MD 7; LSR 15.3,10; OIS 8.1-3.