Datasets to Help Teach Statistics
M-Bias Data
Mediator Data
Causal Quartet Data
Datasaurus Dozen Data
Gelman Heterogeneity Causal Quartet Data
Anscombe's Quartet High Leverage Data
Anscombe's Quartet Linear Data
Anscombe's Quartet Nonlinear Data
Anscombe's Quartet Outlier Data
Anscombe's Quartet Data
Collider Data
Time-varying Causal Quartet Data
Confounder Data
Interaction Triptych Data
Rashomon Quartet Data
Gelman Variation Causal Quartet Data
In the spirit of Anscombe's quartet, this package includes datasets that demonstrate the importance of visualizing your data, the importance of not relying on statistical summary measures alone, and why additional assumptions about the data generating mechanism are needed when estimating causal effects. The package includes "Anscombe's Quartet" (Anscombe 1973) <doi:10.1080/00031305.1973.10478966>, D'Agostino McGowan & Barrett (2023) "Causal Quartet" <doi:10.48550/arXiv.2304.02683>, "Datasaurus Dozen" (Matejka & Fitzmaurice 2017), "Interaction Triptych" (Rohrer & Arslan 2021) <doi:10.1177/25152459211007368>, "Rashomon Quartet" (Biecek et al. 2023) <doi:10.48550/arXiv.2302.13356>, and Gelman "Variation and Heterogeneity Causal Quartets" (Gelman et al. 2023) <doi:10.48550/arXiv.2302.12878>.
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