Regression and Other Stories
Abstract
Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The primary motivation for using propensity score matching in this study was to achieve covariate overlap-making sure that like students are compared.
“However, in the case of this study, while imbalance is not unimportant, the primary motivation for using propensity score matching was to achieve covariate overlap—that is, making sure that like students are compared (Gelman et al., 2020).”
From Improving Academic Performance and Retention of First-Year Biology Students through a Scalable Peer Mentorship Program (2021) · cites this paper as Gelman et al., 2020
Cited by
Sorted by citations
Johansson, F., Rozental, A., Edlund, K., Côté, P., Sundberg, T., Onell, C., Rudman, A., & Skillgate, E. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.49346 · Cited by 124 · see citing works
Herbener, A. B., & Damholdt, M. F. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103409 · Cited by 107 · see citing works
Wilton, M., Katz, D., Clairmont, A., Gonzalez-Nino, E., Foltz, K. R., & Christoffersen, R. E. (2021) · CBE—Life Sciences Education · doi:10.1187/cbe.21-02-0039 · Cited by 20 · see citing works
Davinci Goes to Bebras: A Study on the Problem Solving Ability of GPT-3
Bellettini, C., Lodi, M., Lonati, V., Monga, M., & Morpurgo, A. (2023) · Proceedings of the 15th International Conference on Computer Supported Education · doi:10.5220/0012007500003470 · Cited by 10 · see citing works
Echoes of AI: Investigating the downstream effects of AI assistants on software maintainability
Borg, M., Hewett, D., Hagatulah, N., Couderc, N., Söderberg, E., Graham, D., Kini, U., & Farley, D. (2026) · Empirical Software Engineering · doi:10.1007/s10664-026-10889-1 · Cited by 4 · see citing works
- Authors
- Gelman, A., Hill, J., & Vehtari, A.
- Affiliations
- Columbia University (US)
- New York University (US)
- Aalto University (FI)
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2020-07-23
- OpenAlex
- W3014650038
- Cited by
- 613
- References
- 0
- Citations by year
- 2026: 65 · 2025: 114 · 2024: 125 · 2023: 148 · 2022: 91 · 2021: 61 · 2020: 8 · 2019: 1
- FWCI
- 34.57
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Data Analysis with R
- Keywords
- regression analysis, causal inference, prediction, estimation, sample size, missing data, logistic regression, generalized linear models, observational studies, experimentation, model assumptions, R, Stan, statistical graphics
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 ›
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