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
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About this Course
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- Statistical Inference
- Statistical Hypothesis Testing
- R Programming
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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
Reviews
- 5 stars83.13%
- 4 stars13.27%
- 3 stars1.93%
- 2 stars0.63%
- 1 star1.02%
TOP REVIEWS FROM INFERENTIAL STATISTICS
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,
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!
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.
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.
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