This Inferential Statistics offered by Coursera in partnership with Duke University 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 the Specialization and the Course
- Central Limit Theorem and Confidence Interval
- Inference and Significance
- Inference for Comparing Means
- Inference for Proportions
- Data Analysis Project
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- 1 months
Start dates & application deadlines
DisciplinesMathematics Statistics View 52 other Masters in Statistics in United States
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We are not aware of any academic requirements for this programme.
We are not aware of any English requirements for this programme.
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Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.
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Apply and win up to €10000 to cover your tuition fees.