This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data
About this Course
Skills you will gain
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
- 5 stars83.29%
- 4 stars13.23%
- 3 stars1.96%
- 2 stars0.61%
- 1 star0.89%
TOP REVIEWS FROM INFERENTIAL STATISTICS
What I learned best is not the formula, but the approach to test the conditions, the discussion of source of potential bias, the selection of inferential statistics methods.
This course is an excellent overview of inferential statistic tests / hypothesis tests and confidence intervals. The organization and material is quite good, with exercises and applications using R.
Great course. If you put in a little effort, you will come out with a lot of new knowledge. I recommend using the book after you have seen the movies. It gives a deeper picture of how it works. Great!
I really enjoyed this course and found the professors lectures better structured and clearer. I also like (and needed) the variety of datasets she used for instruction. Thank you!!
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