Abstract
In the last years, there is movement towards simulation-based inference (e.g. bootstrapping and randomization tests) in order to improve students’ understanding of statistical reasoning as well as a call to introduce statistical computing and reproducible analysis within the curriculum. With the help of R mosaic and the concept of minimal R, we were able to include all this in an introductory statistics course for people studying while working a business-related major. Moreover, this also paves the road towards methods and concepts like data wrangling or algorithmic modelling, more related to data science than to classical statistics.
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Notes
- 1.
This questionnaire was used in our first surveys 2015 and 2016 already.
- 2.
Additionally, to the CAOS items, we used a multi-item scale based on the Unified Theory of Acceptance and Use of Technology (UTAUT2 Venkatesh et al. 2012) which is not part of the investigation referred to in this paper.
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Acknowledgements
We thank Oliver Gansser, Bianca Krol, Sebastian Sauer, and numerous other colleagues for their contribution in the proposed change of the curriculum and for helpful comments in order to improve the teaching materials. Also, we thank Nathan Tintle for his support with the CAOS inventory. The remarks of Nicholas Horton, Randall Pruim, and two reviewers helped to improve this paper a lot. We gratefully acknowledge that our work was supported by an internal teaching innovation grant by our institution.
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Lübke, K., Gehrke, M., Markgraf, N. (2019). Statistical Computing and Data Science in Introductory Statistics. In: Bauer, N., Ickstadt, K., Lübke, K., Szepannek, G., Trautmann, H., Vichi, M. (eds) Applications in Statistical Computing. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-25147-5_9
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