Interpreting and Communicating Statistical Results with R - Online Course
An 8-Hour Livestream Seminar Taught by
Vincent Arel-Bundock10:30am-12:30pm (convert to your local time)
1:00pm-3:00pm
This seminar is designed to help you tackle the challenges of understanding and conveying complex statistical results. Coefficient estimates can often be difficult to interpret, and their substantive meaning is not always obvious. This course provides you with the tools and knowledge to overcome these obstacles, making it easier for you to present your findings with clarity and confidence.
Throughout the course, we’ll teach you an easy, consistent, and flexible approach to interpreting a wide range of statistical models in R. You’ll gain a deep understanding of statistical concepts such as predictions, contrasts, marginal means, marginal effects, odds ratios, and risk ratios. The course will introduce you to a powerful toolkit in R, specifically designed to compute and report the most appropriate quantities of interest for your use case.
To reinforce your learning, the course will include hands-on exercises that allow you to apply these concepts in real-world scenarios. By learning how to generate plots and tables to illustrate your findings, you’ll be able to convey your results effectively to your audience and stakeholders, even when dealing with intricate models.
Starting May 28, this seminar will be presented as an 8-hour synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 30-minute break. Live attendance is recommended for the best experience. But if you can’t join in real time, recordings will be available within 24 hours and can be accessed for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about the course content
By the end of this class, you’ll be well-equipped to not only make sense of complex statistical results but also to communicate them in a compelling and engaging manner, ensuring that your research or work has a lasting impact. With a comprehensive understanding of the distinctions between various statistical concepts and the ability to apply an advanced toolkit in R, you’ll be prepared to tackle statistical challenges with confidence.
The course content includes:
-
- Clarifying distinctions between statistical concepts such as predictions, contrasts, marginal means, marginal effects, odds ratios, and risk ratios.
- Introducing a powerful toolkit in R to compute and report the most appropriate quantities of interest for your use case.
- Generating plots and tables to illustrate your findings.
- Creating reproducible reports and slideshows for stakeholders.
You will also be introduced to several useful R packages, including:
-
- marginaleffects: https://marginaleffects.com/
- modelsummary: https://modelsummary.com/
- tinytable: https://vincentarelbundock.git
Blog Post
Check out Dr. Arel-Bundock’s blog post, Analyzing Experiments Using the marginaleffects Package for R, where he describes using the marginaleffects package for R to analyze a 2×2 factorial design.
By the end of this class, you’ll be well-equipped to not only make sense of complex statistical results but also to communicate them in a compelling and engaging manner, ensuring that your research or work has a lasting impact. With a comprehensive understanding of the distinctions between various statistical concepts and the ability to apply an advanced toolkit in R, you’ll be prepared to tackle statistical challenges with confidence.
The course content includes:
-
- Clarifying distinctions between statistical concepts such as predictions, contrasts, marginal means, marginal effects, odds ratios, and risk ratios.
- Introducing a powerful toolkit in R to compute and report the most appropriate quantities of interest for your use case.
- Generating plots and tables to illustrate your findings.
- Creating reproducible reports and slideshows for stakeholders.
You will also be introduced to several useful R packages, including:
-
- marginaleffects: https://marginaleffects.com/
- modelsummary: https://modelsummary.com/
- tinytable: https://vincentarelbundock.git
hub.io/tinytable/
Blog Post
Check out Dr. Arel-Bundock’s blog post, Analyzing Experiments Using the marginaleffects Package for R, where he describes using the marginaleffects package for R to analyze a 2×2 factorial design.
Computing
To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of R installed, together with the tidyverse, marginaleffects, modelsummary, and tinytable libraries. You are also encouraged to download and install RStudio, a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.
Basic familiarity with R is highly desirable, but even novice R coders should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of R installed, together with the tidyverse, marginaleffects, modelsummary, and tinytable libraries. You are also encouraged to download and install RStudio, a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.
Basic familiarity with R is highly desirable, but even novice R coders should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
Who should register?
This course is for anyone who wants to leverage the power of statistical models to deliver clear, compelling insights. If you are a data analyst, researcher, statistician, or data-driven professional who often needs to interpret complex statistical models and communicate findings in an intuitive, comprehensible manner, this course is for you.
While the course is designed to be broadly accessible, the instructor will assume that you have a working knowledge of R programming (at the level of our R for Stata Users, R for SAS Users, R for SPSS Users, or Introduction to R for Data Analysis courses) and linear regression models. A basic understanding of generalized linear models would also help you get the most out of the course, but it is not essential.
This course is for anyone who wants to leverage the power of statistical models to deliver clear, compelling insights. If you are a data analyst, researcher, statistician, or data-driven professional who often needs to interpret complex statistical models and communicate findings in an intuitive, comprehensible manner, this course is for you.
While the course is designed to be broadly accessible, the instructor will assume that you have a working knowledge of R programming (at the level of our R for Stata Users, R for SAS Users, R for SPSS Users, or Introduction to R for Data Analysis courses) and linear regression models. A basic understanding of generalized linear models would also help you get the most out of the course, but it is not essential.
Seminar outline
- The programming environment
-
- R
- RStudio
- Quarto
- R libraries
-
- Modeling: MASS, mgcv, brms
- Interpretation: marginaleffects
- Communication: modelsummary, tinytable
- Statistical building blocks
-
- Predictions
- Comparisons
- Slopes
- Hypothesis and equivalence tests
- Interpretation
-
- Using the marginaleffects package to compute quantities of interest
- Communication
-
- Automatically generating plots to communicate results:
-
- marginaleffects
- Automatically generating tables to summarize datasets and model results:
-
- modelsummary
- tinytable
- The programming environment
-
- R
- RStudio
- Quarto
- R libraries
-
- Modeling: MASS, mgcv, brms
- Interpretation: marginaleffects
- Communication: modelsummary, tinytable
-
-
- Statistical building blocks
-
- Predictions
- Comparisons
- Slopes
- Hypothesis and equivalence tests
-
- Interpretation
-
- Using the marginaleffects package to compute quantities of interest
-
- Communication
-
- Automatically generating plots to communicate results:
-
- marginaleffects
-
- Automatically generating tables to summarize datasets and model results:
-
- modelsummary
- tinytable
-
- Automatically generating plots to communicate results:
-
Payment information
The fee of $695 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $695 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.