The DOI matches its OpenAlex record.
An Introduction to Statistical Learning: with Applications in R
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837
Applications of digital technology in COVID-19 pandemic planning and response
Whitelaw, S., Mamas, M. A., Topol, E., & Van Spall, H. G. C. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30142-4 · Cited by 992
Deep Reinforcement Learning: An Overview
Li, Y. (2017) · arXiv · doi:10.48550/arxiv.1701.07274 · Cited by 762
Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500
Krauss, C., Do, X. A., & Huck, N. (2017) · European Journal of Operational Research · doi:10.1016/j.ejor.2016.10.031 · Cited by 750
Downscaling land surface temperatures at regional scales with random forest regression
Hutengs, C., & Vohland, M. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.03.006 · Cited by 562
Metadata
- Authors
- James, G. M., Witten, D., Hastie, T. J., & Tibshirani, R. J.
- Publisher
- QUBES Educational Resources
- Published
- 2018
- OpenAlex
- W1492815834
- Cited by
- 2,912
- References
- 0
- FWCI
- 331.97
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An Introduction to Statistical Learning: with Applications in R. https://app.cochat.ai/papers/10.25334/q4ht55
- The paper
- James, G. M., Witten, D., Hastie, T. J., & Tibshirani, R. J. (2018). An Introduction to Statistical Learning: with Applications in R. https://doi.org/10.25334/q4ht55
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
