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.
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- 5 stars57.46%
- 4 stars23.19%
- 3 stars10.06%
- 2 stars4.52%
- 1 star4.74%
통계적 추론의 최상위 리뷰
In my opinion, this course is fundamental to Statistics and therefore Machine Learning. It is well explained, although it requires students to work on more mathematical aspect in parallel.
A very conceptual course to understand the fundamentals of Inferential Statistics. I would recommend this course to all aspiring data analysts/scientists or business analysts.
If you work through all the examples, you will be pleasantly surprised. This is an awesome course. Highly recommended. Many thanks to Brian Caffo for improving my understanding.
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
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