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references.bib
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@incollection{calero_valdez_studying_2018,
address = {Cham},
title = {Studying {Biases} in {Visualization} {Research}: {Framework} and {Methods}},
isbn = {978-3-319-95831-6},
shorttitle = {Studying {Biases} in {Visualization} {Research}},
url = {https://doi.org/10.1007/978-3-319-95831-6_2},
abstract = {In this chapter, we propose and discuss a lightweight framework to help organize research questions that arise around biases in visualization and visual analysis. We contrast our framework against the cognitive bias codex by Buster Benson. The framework is inspired by Norman’s Human Action Cycle and classifies biases into three levels: perceptual biases, action biases, and social biases. For each of the levels of cognitive processing, we discuss examples of biases from the cognitive science literature and speculate how they might also be important to the area of visualization. In addition, we put forward a methodological discussion on how biases might be studied on all three levels, and which pitfalls and threats to validity exist. We hope that the framework will help spark new ideas and guide researchers that study the important topic of biases in visualization.},
language = {en},
urldate = {2023-09-20},
booktitle = {Cognitive {Biases} in {Visualizations}},
publisher = {Springer International Publishing},
author = {Calero Valdez, André and Ziefle, Martina and Sedlmair, Michael},
editor = {Ellis, Geoffrey},
year = {2018},
doi = {10.1007/978-3-319-95831-6_2},
pages = {13--27},
}
@misc{cortezWineQuality2009,
title = {Wine {{Quality}}},
author = {Cortez, Paulo and Cerdeira, A and Almeida, F and Matos, T and Reis, J.},
year = {2009},
publisher = {{UCI Machine Learning Repository}},
doi = {10.24432/C56S3T},
abstract = {Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests},
langid = {english},
annotation = {Published: UCI Machine Learning Repository}
}
@incollection{igual_regression_2017,
address = {Cham},
title = {Regression {Analysis}},
isbn = {978-3-319-50017-1},
url = {https://doi.org/10.1007/978-3-319-50017-1_6},
abstract = {In this chapter, we introduce regression analysis and some of its applications in data science using Python tools. We show how regression analysis allows us to understand the behavior of data better, to predict data values (continuous or discrete), and to find important variables by means of building a model from the data. We present four different regression models: simple linear regression, multiple linear regression, polynomial regression and logistic regression. We also emphasize the properties of sparse models in the selection of variables. We use different Python toolboxes to build and apply regression models with ease. Specific visualization tools from Seaborn allow qualitative evaluation; while tools from the Scikit-learn library make quantitative evaluation easier, computing several validation measures. Depending on our aim, visual inspection of the data, statistical analysis or prediction, we chose one tool or another. Regression models are motivated by three real problems that deal with the following questions. Is the climate really changing? Can we predict the price of a new market, given any of its attributes? How many goals makes a football team the winner or the loser?},
booktitle = {Introduction to {Data} {Science}: {A} {Python} {Approach} to {Concepts}, {Techniques} and {Applications}},
publisher = {Springer International Publishing},
author = {Igual, Laura and Seguí, Santi},
year = {2017},
doi = {10.1007/978-3-319-50017-1_6},
pages = {97--114},
}
@article{knuth84,
author = {Knuth, Donald E.},
title = {Literate Programming},
year = {1984},
issue_date = {May 1984},
publisher = {Oxford University Press, Inc.},
address = {USA},
volume = {27},
number = {2},
issn = {0010-4620},
url = {https://doi.org/10.1093/comjnl/27.2.97},
doi = {10.1093/comjnl/27.2.97},
journal = {Comput. J.},
month = may,
pages = {97–111},
numpages = {15}
}
@misc{misc_communities_and_crime_183,
author = {Redmond,Michael},
title = {{Communities and Crime}},
year = {2009},
howpublished = {UCI Machine Learning Repository},
doi = {10.24432/C53W3X},
url = {http://archive.ics.uci.edu/dataset/183/communities+and+crime}
}
@article{pageWhyManyModelThinkers2018,
title = {Why ``{{Many-Model Thinkers}}'' {{Make Better Decisions}}},
author = {Page, Scott E.},
year = {2018},
month = nov,
journal = {Harvard Business Review},
issn = {0017-8012},
urldate = {2023-11-03},
url = {https://hbr.org/2018/11/why-many-model-thinkers-make-better-decisions},
abstract = {Organizations are awash in data \textemdash{} from geocoded transactional data to real-time website traffic to semantic quantifications of corporate annual reports. All these data and data sources only add value if put to use. And that typically means that the data is incorporated into a model. The most sophisticated organizations \textemdash{} from Alphabet to Berkshire Hathaway to the CIA \textemdash{} all use models. In fact, they do something even better: they use many models in combination. But creating an ``ensemble'' of models isn't just about picking the ones that perform best on their own. You want to combine models that complement one another. Three rules can help you construct your own powerful ensemble of models: spread attention broadly, boost predictions, and seek conflict.},
chapter = {Decision making and problem solving},
keywords = {Analytics and data science,Decision making and problem solving}
}
@misc{r_a_fisher_iris_1936,
title = {Iris},
url = {https://archive.ics.uci.edu/dataset/53},
doi = {10.24432/C56C76},
urldate = {2023-09-18},
publisher = {UCI Machine Learning Repository},
author = {Fisher,R. A.},
year = {1936},
note = {{DOI}: https://doi.org/10.24432/C56C76}
}
@article{rouncefield_statistics_1995,
title = {The {Statistics} of {Poverty} and {Inequality}},
volume = {3},
issn = {1069-1898},
url = {https://www.tandfonline.com/doi/full/10.1080/10691898.1995.11910491},
doi = {10.1080/10691898.1995.11910491},
language = {en},
number = {2},
urldate = {2023-09-21},
journal = {Journal of Statistics Education},
author = {Rouncefield, Mary},
month = jul,
year = {1995},
pages = {8},
file = {Rouncefield_1995_The Statistics of Poverty and Inequality.pdf:/Users/u2071219/Shared with me/Zotfiles/Journal of Statistics Education/1995/Rouncefield_1995_The Statistics of Poverty and Inequality.pdf:application/pdf},
}