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Graphical Methods for Data Analysis.
Abstract
This book present graphical methods for analysing data. Some methods are new and some are old, some require a computer and others only paper and pencil; but they are all powerful data analysis tools. In many situations, a set of data � even a large set- can be adequately analysed through graphical methods alone. In most other situations, a few well-chosen graphical displays can significantly enhance numerical statistical analyses.
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Cited by
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Wilkinson, L., & Task Force on Statistical Inference (1999) · American Psychologist · doi:10.1037/0003-066x.54.8.594 · Cited by 2,791
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Wisz, M. S., Hijmans, R. J., Li, J., Peterson, A. T., Graham, C. H., Guisan, A., & NCEAS Predicting Species Distributions Working Group (2008) · Diversity and Distributions · doi:10.1111/j.1472-4642.2008.00482.x · Cited by 2,514
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Hintze, J. L., & Nelson, R. D. (1998) · The American Statistician · doi:10.1080/00031305.1998.10480559 · Cited by 1,802
Combining nonoverlap and trend for single-case research: Tau-U
Parker, R. I., Vannest, K. J., Davis, J. L., & Sauber, S. B. (2011) · Behavior Therapy · doi:10.1016/j.beth.2010.08.006 · Cited by 1,468
Functional imaging of neural responses to expectancy and experience of monetary gains and losses
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Metadata
- Authors
- Fisher, N. I., Chambers, J. M., Cleveland, W. S., Kleiner, B., & Tukey, P. A.
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1984-06
- Volume
- 40
- Issue
- 2
- Pages
- 567
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W2025320861
- Cited by
- 1,982
- References
- 0
- FWCI
- 37.10
- Type
- journal article
- Language
- en
- Open access
- closed
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- Fisher, N. I., Chambers, J. M., Cleveland, W. S., Kleiner, B., & Tukey, P. A. (1984). Graphical Methods for Data Analysis. Biometrics. https://doi.org/10.2307/2531418
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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.
