Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.
- 5 stars57.46%
- 4 stars23.19%
- 3 stars10.06%
- 2 stars4.52%
- 1 star4.74%
This was probably the most difficult and challenging course . Had to pull out my old stats books to remember most of it. Using R to do what we used to do with TI-83's was great!
Loved the course, also very pleased that there was recommended reading for further study. Also loved Brian Caffo's deadpan joke delivery, really hard to know if that's an act ;)
This course is slightly difficult, and to attempt the quizzes and the project, the student must do some more external research...
Otherwise, great introduction to statistics!
The strategy for model selection in multivariate environment should have been explained with an example. This will make the model selection process, interaction and its interpretation more clear.