RiseUpp Logo
Educator Logo

Basics of Statistical Inference and Modelling Using R

This course is part of Statistical Analysis in R.

This comprehensive course provides a practical introduction to statistical inference and modeling using R programming. Students learn theoretical foundations and hands-on implementation of key statistical concepts, including sampling distributions, hypothesis testing, ANOVA, and multivariate analysis. The course emphasizes understanding both why methods work and when to apply them, combining theoretical knowledge with practical R programming skills. Designed for those with limited statistical background, it covers experimental design, data visualization, and advanced numerical methods.

4.6

(7 ratings)

8,199 already enrolled

Instructors:

English

English

Powered by

Provider Logo
Basics of Statistical Inference and Modelling Using R

This course includes

6 Weeks

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

26,841

Audit For Free

What you'll learn

  • Master fundamental concepts of statistical inference and sampling distributions

  • Develop proficiency in hypothesis testing and p-value interpretation

  • Learn to perform and interpret ANOVA and regression analyses

  • Gain practical skills in data visualization using R

  • Understand experimental design and power analysis

  • Master numerical methods including simulations and bootstrap techniques

Skills you'll gain

Statistical Analysis
R Programming
Data Analysis
Hypothesis Testing
ANOVA
Regression Analysis
Data Visualization
Experimental Design
Statistical Inference
Statistical Modeling

This course includes:

PreRecorded video

Graded assignments, Exams

Access on Mobile, Tablet, Desktop

Limited Access access

Shareable certificate

Closed caption

Get a Completion Certificate

Share your certificate with prospective employers and your professional network on LinkedIn.

Provided by

Certificate

Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

icon-0icon-1icon-2icon-3icon-4

Module Description

This course offers comprehensive training in statistical inference and modeling using R programming. Students learn fundamental concepts of statistical analysis, including sampling distributions, hypothesis testing, and multivariate analysis. The curriculum combines theoretical understanding with practical implementation, covering experimental design, data visualization, and advanced statistical methods. Through hands-on exercises and real-world applications, participants develop skills in both statistical theory and R programming.

Fee Structure

Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Statistical Analysis in R

Instructor

A Distinguished Leader in Statistical Modeling and Epidemiology

Elena Moltchanova serves as Professor of Statistics and Head of the Statistical Consulting Unit at the University of Canterbury, where she has established herself as a leading expert in applied Bayesian statistics and spatial modeling. Her academic journey began at the University of Helsinki, Finland, where she completed her MSc in Statistics, followed by a PhD from the University of Jyvaskyla focusing on applications of spatial statistics in epidemiology. Her career includes significant contributions at IIASA's Ecosystems Services and Management Program, where she first joined as a Young Scientists Summer Program participant in 2001, earning the Michailevich Scholarship for her work on image restoration. Since joining the University of Canterbury in 2011, she has published extensively, with over 50 papers spanning epidemiology of chronic diseases, extreme event modeling, and forest ecology. Her research impact is evidenced by over 9,300 citations and an h-index of 40. Her expertise spans multiple statistical software platforms and programming languages, which she applies to diverse fields including climate modeling, rare extreme events analysis, and epidemiological studies. As Head of the Statistical Consulting Unit, she continues to bridge theoretical statistics with practical applications while maintaining active research collaborations across international institutions.

Basics of Statistical Inference and Modelling Using R

This course includes

6 Weeks

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

26,841

Audit For Free

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.

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.