Master fundamental statistical modeling techniques through hands-on practice with R, from simple linear regression to multiple predictors.
Master fundamental statistical modeling techniques through hands-on practice with R, from simple linear regression to multiple predictors.
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
(1,706 ratings)
97,194 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 3 more
What you'll learn
Understand correlation and linear relationships
Build and interpret regression models
Perform statistical inference for regression
Analyze multiple predictor relationships
Diagnose and validate regression models
Skills you'll gain
This course includes:
2.1 Hours PreRecorded video
8 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course covers linear regression techniques using R. Students learn to assess relationships between variables, build predictive models, and evaluate model performance. The curriculum includes simple and multiple linear regression, model diagnostics, and practical applications through hands-on exercises with RStudio.
About Linear Regression and Modeling
Module 1 · 0 Hours to complete
Linear Regression
Module 2 · 2 Hours to complete
More about Linear Regression
Module 3 · 2 Hours to complete
Multiple Regression
Module 4 · 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
1,706 ratings
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