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Linear Regression and Modeling

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)

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پښتو, বাংলা, اردو, 3 more

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Linear Regression and Modeling

This course includes

10 Hours

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

Free course

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

Statistics
Linear Regression
R Programming
Data Analysis
Model Selection
Statistical Inference
Multiple Regression
Data Visualization
Model Diagnostics
Variable Relationships

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

Mine Çetinkaya-Rundel
Mine Çetinkaya-Rundel

4.8 rating

764 Reviews

4,02,190 Students

9 Courses

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.

Linear Regression and Modeling

This course includes

10 Hours

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

Free course

Testimonials

Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.

4.8 course rating

1,706 ratings

Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.