Master essential statistical methods for data analysis using R, from hypothesis testing to confidence intervals for beginners.
Master essential statistical methods for data analysis using R, from hypothesis testing to confidence intervals for beginners.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Analysis with R Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.8
(2,670 ratings)
1,18,653 already enrolled
Instructors:
English
বাংলা, اردو, Tiếng Việt, 2 more
What you'll learn
Understand and apply statistical inference methods
Perform hypothesis testing with confidence
Use R for statistical analysis
Interpret and report statistical results
Work with numerical and categorical data
Skills you'll gain
This course includes:
5.4 Hours PreRecorded video
12 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course covers key statistical inference methods for both numerical and categorical data. Students learn to perform hypothesis tests, interpret p-values, and report results effectively. The curriculum includes practical application using R and RStudio through hands-on lab exercises and projects, focusing on both theoretical understanding and real-world data analysis skills.
About the Specialization and the Course
Module 1 · 0 Hours to complete
Central Limit Theorem and Confidence Interval
Module 2 · 3 Hours to complete
Inference and Significance
Module 3 · 3 Hours to complete
Inference for Comparing Means
Module 4 · 3 Hours to complete
Inference for Proportions
Module 5 · 5 Hours to complete
Fee Structure
Instructor
Innovator in Statistics Education and Research
Mine Çetinkaya-Rundel is an Associate Professor of the Practice in the Department of Statistical Science at Duke University. She earned her Ph.D. in Statistics from the University of California, Los Angeles, and holds a B.S. in Actuarial Science from New York University's Stern School of Business. Dr. Çetinkaya-Rundel is passionate about enhancing statistics pedagogy through innovative teaching methods. Her recent work emphasizes developing student-centered learning tools for introductory statistics courses, integrating computational skills with a focus on reproducibility, and addressing the gender gap in self-efficacy within STEM fields. Additionally, her research encompasses spatial modeling of survey, public health, and environmental data. As a co-author of "OpenIntro Statistics" and a key contributor to the OpenIntro project, she is dedicated to creating open-licensed educational resources that reduce barriers to learning. Dr. Çetinkaya-Rundel also engages with the broader statistical community as a co-editor of the Citizen Statistician blog and a contributor to the "Taking a Chance in the Classroom" column in Chance Magazine, furthering her commitment to accessible and impactful statistics education.
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4.8 course rating
2,670 ratings
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