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
- Statistical Inference
- Statistical Hypothesis Testing
- R Programming
Syllabus - What you will learn from this course
About the Specialization and the Course
Central Limit Theorem and Confidence Interval
Inference and Significance
Inference for Comparing Means
- 5 stars83.13%
- 4 stars13.27%
- 3 stars1.93%
- 2 stars0.63%
- 1 star1.02%
TOP REVIEWS FROM INFERENTIAL STATISTICS
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.
Very well taught. Student given an opportunity to explore and search for ways to solve problems by themselves. Professor (mentor) and other students always ready to help should you get stuck!
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 by Professor Çetinkaya-Rundel is awesome because it is taught in a very clear and vivid way. Lab section and forum are so dope that I love them so much! Definitely strong recommendation!!!
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