This talk was presented as part of JuliaCon 2021. Abstract: Regression models are useful but they can be tricky to interpret. Variable centering and contrast coding can obscure the meaning of main effects. Interaction terms, especially higher order ones, only increase the difficulty of interpretation. Here, we introduce Effects.jl which translates the fitted model, including estimated uncertainty, back into data space. Using Effects.jl, it is possible to generate effects plots that enable rapid visualization and interpretation of regression models. For more info on the Julia Programming Language, follow us on Twitter: https://twitter.com/JuliaLanguage and consider sponsoring us on GitHub: https://github.com/sponsors/JuliaLang Contents 0:00 Welcome! 0:14 Analyzing concrete data using linear regression 0:57 Two-way interaction model 1:38 Finding prediction and effects using given data 4:11 Predictions and effect are invariant to contrast coding 5:01 Three-way interaction model 6:55 Summary S/O to https://github.com/KZiemian for the video timestamps! Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/JuliaCommunity/YouTubeVideoTimestamps Interested in improving the auto generated captions? Get involved here: https://github.com/JuliaCommunity/YouTubeVideoSubtitles