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
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- 5 stars83.29%
- 4 stars13.18%
- 3 stars1.98%
- 2 stars0.60%
- 1 star0.93%
TOP REVIEWS FROM INFERENTIAL STATISTICS
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 concepts are explained in a very simple and effective manner with the help of a case study. Background knowledge of R will be very handy if one wants to cover the topics at a faster rate.
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 final project does not help, for example someone used discrete data 1,2,3,4,5 .... ,40 to compute a p.value as if it was normal. It is too general and does not fill the purpose of the course.
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