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
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 stars83.23%
- 4 stars13.25%
- 3 stars1.99%
- 2 stars0.61%
- 1 star0.89%
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.
I learnt a lot about inferential statistics from this course. It help me to understand better why I used one inferential method instead of another, and the assumptions and conditions.
This is a wonderfully curated course if u follow the readings and practise suggestions. But the main issue is the R programming. It needs better practise than suggested readings.
The course is very well explained I had to refer other materials for ANOVA technique to understand it better hence that part can be either improved OR more reference material be provided
Cost of the Course
Can I just enroll in a single course? I'm not interested in the entire Specialization.
Will I receive a transcript from Duke University for completing this course?