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 stars57,44 %
- 4 stars23,15 %
- 3 stars10,07 %
- 2 stars4,53 %
- 1 star4,78 %
Principales reseñas sobre INFERENCIA ESTADÍSTICA
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.
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!
Statistics was not, to put it mildly, my favorite subject in college. This class, however, managed to actually get me involved in the subject as it is tought with applicability in mind. Thank you.
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.
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