Statistical Inference

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8/10 stars
based on  2 reviews
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Course Details

Cost

FREE,
Add a Verified Certificate for $49

Upcoming Schedule

  • Upcoming

Course Provider

Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4708 reviews

Course Description

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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Reviews 8/10 stars
2 Reviews for Statistical Inference

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Nirav Desai profile image
Nirav Desai profile image
10/10 starsCompleted
  • 9 reviews
  • 9 completed
3 years, 3 months ago
This was a very useful course in Statistical Inference where in all the fundamentals are taught in a very clear manner, setting the stage for the subsequent course on regression models. The course is taught with examples on use of Statistical Inference in biostatistics and these examples are very helpful in understanding the concepts.
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6/10 starsTaking Now
  • 2 reviews
  • 1 completed
3 years, 4 months ago
The course seems to be designed and presented with the assumption that the audience has prior knowledge. There is too much unnecessary jargon. some of it is required as it is after all statistics, but the class imho would benefit further if dropped down a notch or two in terms of delivery complexity.
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