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%
I found this course really good introduction to statistical inference. I did find it quite challenging but I can go away from this course having a greater understanding of Statistical Inference
For starters, it will demand a lot of out of class studies. It took me three months to go through the basics in Khan Academy before attempting it - and after that it was straight forward.
Course is compressed and good to learn in short span. The illustrations and projects are really helpful to learn the concepts and implement. I really enjoyed through the course
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!