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
- 5 stars83.12%
- 4 stars13.30%
- 3 stars1.94%
- 2 stars0.63%
- 1 star0.99%
Excellent course and specialization. I have learnt a lot. Could you also add generalize linear regressoin including logistic, poisson, negative bionomial and survival analysis. Thanks,
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
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.
The professor is one of the best instructers I've seen. I've struggled to understand these concepts before but this course just set everything straight. Lots of content to practice with too.
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?