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@misc{16KolmogorovSmirnovGoodnessofFit,
title = {1.3.5.16. {{Kolmogorov-Smirnov Goodness-of-Fit Test}}},
urldate = {2022-05-20},
howpublished = {https://www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm},
keywords = {01. Goal: descriptive,01. Goal: exploratory or hypothesis generating,01. Goal: inferential or hypothesis testing,02. Causation: association (none implied),02. Causation: none (descriptive),03. Paper type: guide to use,03. Paper type: math description,04. Interpretation and meaning: diagnostics fitting,04. Interpretation and meaning: exploratory,04. Interpretation and meaning: plotting/visualization,04. Interpretation and meaning: significance,05. Interpretation and meaning: meeting assumptions,14. Predictor variables: none (no association),15. Response variables: univariate,16. Response variables: interval,16. Response variables: numeric,16. Response variables: ratio,18. Predictor variables: univariate,21. Missing data: none (complete cases) balanced design,22. Data assumptions: nonparametric,22. Data assumptions: parametric,23. Distributions: simulated randomized computational,23. Distributions: theoretical existing known,25. Domain: A General Works,26. Software: General (applies to any),27. Philosophy: frequentist},
file = {C:\Users\curr0024\Zotero\storage\Y5YKHG4Y\eda35g.html}
}
@incollection{2WayCrossedClassification1997,
title = {The 2-{{Way Crossed Classification}}},
booktitle = {Linear {{Models}}},
year = {1997},
pages = {261--331},
publisher = {John Wiley \& Sons, Ltd},
doi = {10.1002/9781118491782.ch7},
urldate = {2024-05-13},
abstract = {This chapter contains sections titled: The 2-way Classification Without Interaction The 2-Way Classification With Interaction Interpretation of Hypotheses Connectedness {$\mu$}ij-Models Exercises},
chapter = {7},
isbn = {978-1-118-49178-2},
langid = {english},
keywords = {00. Unread},
file = {C\:\\Users\\curr0024\\Zotero\\storage\\FHFXBB7Q\\1997 - The 2-Way Crossed Classification.pdf;C\:\\Users\\curr0024\\Zotero\\storage\\KIE68GDU\\9781118491782.html}
}
@article{a.lee-yawSpeciesDistributionModels,
title = {Species Distribution Models Rarely Predict the Biology of Real Populations},
author = {{A. Lee-Yaw}, Julie and L. McCune, Jenny and Pironon, Samuel and N. Sheth, Seema},
journal = {Ecography},
volume = {n/a},
number = {n/a},
issn = {1600-0587},
doi = {10.1111/ecog.05877},
urldate = {2022-01-03},
abstract = {Species distribution models (SDMs) are widely used in ecology. In theory, SDMs capture (at least part of) species' ecological niches and can be used to make inferences about the distribution of suitable habitat for species of interest. Because habitat suitability is expected to influence population demography, SDMs have been used to estimate a variety of population parameters, from occurrence to genetic diversity. However, a critical look at the ability of SDMs to predict independent data across different aspects of population biology is lacking. Here, we systematically reviewed the literature, retrieving 201 studies that tested predictions from SDMs against independent assessments of occurrence, abundance, population performance, and genetic diversity. Although there is some support for the ability of SDMs to predict occurrence ( 53\% of studies depending on how support was assessed), the predictive performance of these models declines progressively from occurrence to abundance, to population mean fitness, to genetic diversity. At the same time, we observed higher success among studies that evaluated performance for single versus multiple species, pointing to a possible publication bias. Thus, the limited accuracy of SDMs reported here may reflect the best-case scenario. We discuss the limitations of these models and provide specific recommendations for their use for different applications going forward. However, we emphasize that predictions from SDMs, especially when used to inform conservation decisions, should be treated as hypotheses to be tested with independent data rather than as stand-ins for the population parameters we seek to know.},
langid = {english},
keywords = {00. Unread,01. Goal: predictive or mechanistic,02. Causation: association (none implied),02. Causation: cause-and-effect,02. Causation: none (descriptive),04. Interpretation and meaning: diagnostics fitting,25. Domain: GB Physical geography,25. Domain: GE Environmental Sciences,25. Domain: Q Science (General),25. Domain: QH Natural History - Biology,25. Domain: QK Botany,25. Domain: QL Zoology},
file = {C\:\\Users\\curr0024\\Zotero\\storage\\9AUQZPL6\\A. Lee-Yaw et al. - Species distribution models rarely predict the bio.pdf;C\:\\Users\\curr0024\\Zotero\\storage\\ILDRLP7J\\ecog.html}
}
@article{abadiAtmosphericDustStimulated2019,
title = {Atmospheric Dust Stimulated Marine Primary Productivity during {{Earth}}'s Penultimate Icehouse},
author = {Abadi, Mehrdad Sardar and Owens, Jeremy D. and Liu, Xiaolei and Them, II, Theodore R. and Cui, Xingqian and Heavens, Nicholas G. and Soreghan, Gerilyn S.},
year = {2019},
month = dec,
journal = {Geology},
volume = {48},
number = {3},
pages = {247--251},
issn = {0091-7613},
doi = {10.1130/G46977.1},
urldate = {2023-11-15},
abstract = {The importance of dust as a source of iron (Fe) for primary production in modern oceans is well studied but remains poorly explored for deep time. Vast dust deposits are well recognized from the late Paleozoic and provisionally implicated in primary production through Fe fertilization. Here, we document dust impacts on marine primary productivity in Moscovian (Pennsylvanian, ca. 307 Ma) and Asselian (Permian, ca. 295 Ma) carbonate strata from peri-Gondwanan terranes of Iran. Autotrophic contents of samples, detected by both point-count and lipid-biomarker analyses, track concentrations of highly reactive Fe, consistent with the hypothesis that dust stimulated primary productivity, also promoting carbonate precipitation. Additionally, highly reactive Fe tracks the fine-dust fraction. Dust-borne Fe fertilization increased organic and inorganic carbon cycling in low- and mid-latitude regions of Pangaea, maintaining low pCO2.},
keywords = {00. Ready for website,01. Goal: descriptive,01. Goal: inferential or hypothesis testing,02. Causation: cause-and-effect,02. Causation: none (descriptive),03. Paper type: good example,04. Interpretation and meaning: plotting/visualization,12. Study design: dimension/variable reduction,13. Relationship: linear,15. Response variables: multivariate,16. Response variables: frequency,16. Response variables: integers,16. Response variables: interval,16. Response variables: numeric,18. Predictor variables: multivariate,18. Predictor variables: univariate,19. Predictor variables: categorical,19. Predictor variables: frequency,19. Predictor variables: numeric,20. Predictor variables: interval,20. Predictor variables: ratio,21. Missing data: none (complete cases) balanced design,21. Missing data: whole rows missing / unbalanced design,22. Data assumptions: parametric,23. Distributions: bounded,23. Distributions: homoscedasticity,23. Distributions: simulated randomized computational,25. Domain: QD Chemistry,25. Domain: QE Geology,26. Software: R or S Plus,27. Philosophy: frequentist},
file = {C\:\\Users\\curr0024\\Zotero\\storage\\QQBVLCFV\\Abadi et al. - 2019 - Atmospheric dust stimulated marine primary product.pdf;C\:\\Users\\curr0024\\Zotero\\storage\\4GCTCMDY\\Atmospheric-dust-stimulated-marine-primary.html}
}
@misc{AccessibilitypostersPdf,
title = {Accessibility-Posters.Pdf},
urldate = {2021-05-26},
howpublished = {https://ukhomeoffice.github.io/accessibility-posters/posters/accessibility-posters.pdf},
keywords = {00. User interface,24. Super helpful},
file = {C:\Users\curr0024\Zotero\storage\RWUDB7DG\accessibility-posters.pdf}
}
@inproceedings{aderFormalizationStatisticalExpert1991,
title = {Formalization of Statistical Expert Knowledge},
booktitle = {Applied Stochastic Models and Data},
author = {Ader, H.J.},
year = {1991},
pages = {1--14},
address = {Granada, Spain},
urldate = {2020-12-09},
keywords = {00. For my literature review},
file = {C:\Users\curr0024\Zotero\storage\DIK4UGTS\illiad.pdf}
}
@misc{AdviceBlindTeachers,
title = {Advice {{From Blind Teachers}} on {{How}} to {{Teach Statistics}} to {{Blind Students}}},
issn = {1069-1898},
urldate = {2024-04-29},
howpublished = {https://www.tandfonline.com/doi/epdf/10.1080/10691898.2015.11889746?needAccess=true},
langid = {english},
keywords = {00. Accessibility,00. Software development,03. Paper type: how to teach},
file = {C:\Users\curr0024\Zotero\storage\UD4J96F5\10691898.2015.html}
}
@article{albrechtMultivariateAnalysisStudy1980,
title = {Multivariate Analysis and the Study of Form, with Special Reference to Canonical Variate Analysis},
author = {Albrecht, Gene H.},
year = {1980},
month = nov,
journal = {American Zoologist},
volume = {20},
number = {4},
pages = {679--693},
issn = {0003-1569},
doi = {10.1093/icb/20.4.679},
urldate = {2020-12-07},
langid = {english},
file = {C:\Users\curr0024\Zotero\storage\6BI5RGUE\Albrecht - 1980 - Multivariate Analysis and the Study of Form, with .pdf}
}
@misc{alexanderGlhtEmmeansReturning2020,
type = {Forum Post},
title = {Glht and Emmeans Returning Crazy Compact Letters for Unbalanced Dataset in {{R}}},
author = {Alexander},
year = {2020},
month = jul,
journal = {Stack Overflow},
urldate = {2022-09-14},
keywords = {04. Interpretation and meaning: confidence intervals prediction intervals parameter estimation,04. Interpretation and meaning: multiple comparisons,04. Interpretation and meaning: plotting/visualization,04. Interpretation and meaning: significance,15. Response variables: univariate,16. Response variables: numeric,16. Response variables: ordinal,18. Predictor variables: multivariate,18. Predictor variables: univariate,19. Predictor variables: categorical,19. Predictor variables: numeric,23. Distributions: theoretical existing known,26. Software: R or S Plus,27. Philosophy: frequentist},
file = {C:\Users\curr0024\Zotero\storage\CVR5AALC\glht-and-emmeans-returning-crazy-compact-letters-for-unbalanced-dataset-in-r.html}
}
@book{alexanderTellingStoriesData2023,
title = {Telling Stories with Data: With Applications in {{R}}},
shorttitle = {Telling Stories with Data},
author = {Alexander, Rohan},
year = {2023},
series = {Chapman \& {{Hall}}/{{CRC}} Data Science Series},
edition = {First edition},
publisher = {CRC Press},
address = {Boca Raton},
abstract = {"The book equips students with the end-to-end skills needed to do data science. That means gathering, cleaning, preparing, and sharing data, then using statistical models to analyse data, writing about the results of those models, drawing conclusions from them, and finally, using the cloud to put a model into production, all done in a reproducible way. At the moment, there are a lot of books that teach data science, but most of them assume that you already have the data. This book fills that gap by detailing how to go about gathering datasets, cleaning and preparing them, before analysing them. There are also a lot of books that teach statistical modelling, but few of them teach how to communicate the results of the models and how they help us learn about the world. Very few data science textbooks cover ethics, and most of those that do, have a token ethics chapter. Finally, reproducibility is not often emphasised in data science books. This book is based around a straight-forward workflow conducted in an ethical and reproducible way: gather data, prepare data, analyse data, and communicate those findings. This book will achieve the goals by working through extensive case studies in terms of gathering and preparing data, and integrating ethics throughout. It is specifically designed around teaching how to write about the data and models, so aspects such as writing are explicitly covered. And finally, the use of GitHub and the open-source statistical language R are built in throughout the book"--},
isbn = {978-1-00-322940-7},
lccn = {QA76.9.I52}
}
@article{alfaroAdabagPackageClassification2013,
title = {Adabag: {{An R}} Package for Classification with Boosting and Bagging},
shorttitle = {Adabag},
author = {Alfaro, Esteban and Gamez, Matias and Garcia, Noelia and others},
year = {2013},
journal = {Journal of Statistical Software},
volume = {54},
number = {2},
pages = {1--35},
urldate = {2017-02-24},
file = {C:\Users\curr0024\Zotero\storage\U3KFBWUQ\adabag_An_R_Package_for_Classification_with_Boosting_and_Bagging.pdf}
}
@article{alfaroPackageAdabag2015,
title = {Package `Adabag'},
author = {Alfaro, Esteban and Gamez, Matias and Garcia, Noelia and Alfaro, Maintainer Esteban},
year = {2015},
journal = {URL: https://cran. rproject. org/web/packages/adabag/adabag. pdf},
urldate = {2017-03-02},
file = {C:\Users\curr0024\Zotero\storage\VXKRCTQW\adabag.pdf}
}
@misc{allisonhorstHeresMyBeginner2021,
type = {Tweet},
title = {Here's My Beginner Intro to Exploring Missing Values, in Case Any {{DS}} Teachers Want Some Drop-in Material (Lecture+learnr Tutorial Feat. @nj\_tierney's Naniar) 💗 👾learnr Tutorial: {{https://t.co/H38yTEHQKC}} ⏯{{️Slides}}: {{https://t.co/CYTu52IBnL}} 📽{{️Video}}: {{https://t.co/EFqBiRM8JH}}},
author = {{Allison Horst}},
year = {2021},
month = jan,
journal = {@allison\_horst},
urldate = {2021-05-25},
langid = {english},
keywords = {00. Unread,03. Paper type: guide to use,03. Paper type: math description,21. Missing data: none (complete cases) balanced design,21. Missing data: partial cases (pattern),21. Missing data: partial cases (random cells missing),26. Software: R or S Plus},
file = {C:\Users\curr0024\Zotero\storage\CV9R5VXS\1354584136413929473.html}
}
@book{AllNonspringerTitles,
title = {All Non-Springer Titles from Here: {{http://highstat.com/index.php/books}}},
keywords = {00. Potential purchase - in progress ABS}
}
@misc{ameliamcnamaraSpring2020Fall2021,
type = {Tweet},
title = {In {{Spring}} 2020 and {{Fall}} 2020, {{I}} Taught Two Sections of {{R}} Labs for Introductory Statistics. {{One}} Section Was in Formula Syntax, the Other in Tidy(Verse) Syntax. {{I}}'m Writing a Paper about the Experience, but for Now, a Thread 🧵},
author = {{Amelia McNamara}},
year = {2021},
month = jan,
journal = {@AmeliaMN},
urldate = {2021-05-25},
langid = {english},
keywords = {26. Software: R or S Plus},
file = {C:\Users\curr0024\Zotero\storage\R2YH3C2N\1347325295435530241.html}
}
@misc{americanmathematicalsocietyCurrentIndexStatistics2021,
type = {Index},
title = {Current {{Index}} to {{Statistics}} ({{CIS}})},
author = {American Mathematical Society},
year = {2021},
urldate = {2021-12-20},
abstract = {The Current Index to Statistics (CIS) is a bibliographic index of publications in statistics, probability, and related fields. It includes data from approximately 160 core journals, 1,200 additional journals in related fields, and 11,000 books. The bulk of the content in CIS is from 1975-2017.},
howpublished = {https://mathscinet.ams.org/cis},
keywords = {03. Paper type: general reference},
file = {C:\Users\curr0024\Zotero\storage\GYSFWD5J\cis.html}
}
@incollection{AnalysisCovariance2004,
title = {Analysis of {{Covariance}}},
booktitle = {Statistics for {{Research}}},
year = {2004},
pages = {409--429},
publisher = {John Wiley \& Sons, Ltd},
doi = {10.1002/0471477435.ch13},
urldate = {2023-05-30},
abstract = {This chapter contains sections titled: Combining Regression with ANOVA One-Way Analysis of Covariance Testing the Assumptions for Analysis of Covariance Multiple-Comparison Procedures Review Exercises Selected Readings},
chapter = {13},
isbn = {978-0-471-47743-3},
langid = {english},
file = {C\:\\Users\\curr0024\\Zotero\\storage\\GTV6PKML\\2004 - Analysis of Covariance.pdf;C\:\\Users\\curr0024\\Zotero\\storage\\PA3XIWBZ\\0471477435.html}
}
@misc{AnalysisEcologicalCommunities,
title = {Analysis of {{Ecological Communities}}},
journal = {Wild Blueberry Media LLC},
urldate = {2022-12-01},
abstract = {Analysis of Ecological Communities is a book by Bruce McCune , James B. Grace , and Dean L. Urban on methods for analyzing multivariate data in community ecology, published by MjM Software Design, 2002. Bruce McCune is a professor of Department of Botany \& Plant Pathology at Oregon State},
howpublished = {https://www.wildblueberrymedia.net/store/analysis-of-ecological-communities},
langid = {american},
keywords = {00. Potential purchase - upcoming},
file = {C:\Users\curr0024\Zotero\storage\86C8968K\analysis-of-ecological-communities.html}
}
@incollection{AnalysisofVarianceModel2004,
title = {The {{Analysis-of-Variance Model}}},
booktitle = {Statistics for {{Research}}},
year = {2004},
pages = {317--339},
publisher = {John Wiley \& Sons, Ltd},
doi = {10.1002/0471477435.ch11},
urldate = {2023-05-30},
abstract = {This chapter contains sections titled: Random Effects and Fixed Effects Testing the Assumptions for ANOVA Transformations Review Exercises Selected Readings},
chapter = {11},
isbn = {978-0-471-47743-3},
langid = {english},
file = {C\:\\Users\\curr0024\\Zotero\\storage\\RNDSFJKB\\2004 - The Analysis-of-Variance Model.pdf;C\:\\Users\\curr0024\\Zotero\\storage\\U7KFJPIK\\0471477435.html}
}
@book{andersenModernMethodsRobust2007,
title = {Modern Methods for Robust Regression},
author = {Andersen, R.},
year = {2007},
number = {7},
publisher = {Sage Publications, Incorporated},
urldate = {2012-11-11}
}
@article{anderson-cookInClassDemonstrationHelp1999,
title = {An {{In-Class Demonstration}} to {{Help Students Understand Confidence Intervals}}},
author = {{Anderson-Cook}, Christine M.},
year = {1999},
month = nov,
journal = {Journal of Statistics Education},
volume = {7},
number = {3},
publisher = {Taylor \& Francis},
issn = {null},
doi = {10.1080/10691898.1999.12131277},
urldate = {2024-04-29},
abstract = {This article discusses an active learning technique that can be easily incorporated into a variety of introductory statistics classes to demonstrate purely subjective and statistical confidence intervals. The concepts of confidence intervals, confidence levels, and the fixed, but unknown, population parameter are frequently misunderstood by a significant proportion of students. This class activity demonstrates these concepts by stressing the objective nature of statistical confidence intervals. It also emphasizes that the precision of the interval depends on the quality of the data used in its construction. The proposed exercise takes less than 50 minutes of lecture time and helps to solidify these essential statistical concepts in a visual and memorable way. Student reaction to the exercise has been positive as measured anecdotally by both improved student understanding of the concepts and increased interest in the activity.},
keywords = {Sent to CAS1553},
file = {C:\Users\curr0024\Zotero\storage\E3FNYKVD\Anderson-Cook - 1999 - An In-Class Demonstration to Help Students Underst.pdf}
}
@article{andersonAvoidingPitfallsWhen2002,
title = {Avoiding Pitfalls When Using Information-Theoretic Methods},
author = {Anderson, David R. and Burnham, Kenneth P},
year = {2002},
journal = {Journal of Wildlife Management},
volume = {66},
number = {3},
pages = {912--918},
doi = {10.2307/3803155},
keywords = {00. Ready for website,01. Goal: inferential or hypothesis testing,02. Causation: cause-and-effect,03. Paper type: guide to use,04. Interpretation and meaning: model selection + information theoretic,05. Interpretation and meaning: meeting assumptions,10. Study design: interactions,11. Study design: covariates,13. Relationship: linear,15. Response variables: univariate,18. Predictor variables: multivariate,21. Missing data: none (complete cases) balanced design,21. Missing data: partial cases (random cells missing),23. Distributions: theoretical existing known,24. Super helpful,25. Domain: QH Natural History - Biology,25. Domain: QK Botany,25. Domain: QL Zoology,26. Software: General (applies to any),27. Philosophy: evidential statistics,27. Philosophy: frequentist},
file = {C:\Users\curr0024\Zotero\storage\EDNAJD8T\Pitfalls.pdf}
}
@article{andersonNewMethodNonparametric2001,
title = {A New Method for Non-Parametric Multivariate Analysis of Variance},
author = {Anderson, M. J.},
year = {2001},
journal = {Austral Ecology},
volume = {26},
number = {1},
pages = {32--46},
urldate = {2012-08-27},
abstract = {Hypothesis-testing methods for multivariate data are needed to make rigorous probability statements about the effects of factors and their interactions in experiments. Analysis of variance is particularly powerful for the analysis of univariate data. The traditional multivariate analogues, however, are too stringent in their assumptions for most ecological multivariate data sets. Non-parametric methods, based on permutation tests, are preferable. This paper describes a new non-parametric method for multivariate analysis of variance, after McArdle and Anderson (in press). It is given here, with several applications in ecology, to provide an alternative and perhaps more intuitive formulation for ANOVA (based on sums of squared distances) to complement the description provided by McArdle and Anderson (in press) for the analysis of any linear model. It is an improvement on previous non-parametric methods because it allows a direct additive partitioning of variation for complex models. It does this while maintaining the flexibility and lack of formal assumptions of other non-parametric methods. The teststatistic is a multivariate analogue to Fisher's F-ratio and is calculated directly from any symmetric distance or dissimilarity matrix. P-values are then obtained using permutations. Some examples of the method are given for tests involving several factors, including factorial and hierarchical (nested) designs and tests of interactions.},
langid = {english},
keywords = {01. Goal: inferential or hypothesis testing,02. Causation: cause-and-effect,05. Interpretation and meaning: meeting assumptions,10. Study design: interactions,15. Response variables: multivariate,16. Response variables: numeric,18. Predictor variables: multivariate,18. Predictor variables: univariate,19. Predictor variables: categorical,22. Data assumptions: nonparametric,23. Distributions: simulated randomized computational},
file = {C:\Users\curr0024\Zotero\storage\GE2IDBC3\Anderson - A new method for non-parametric multivariate analy.pdf}
}
@article{andersonPermutationTestsMultifactorial2003,
title = {Permutation Tests for Multi-Factorial Analysis of Variance},
author = {Anderson, Marti and Braak, Cajo Ter},
year = {2003},
month = jan,
journal = {Journal of Statistical Computation and Simulation},
volume = {73},
number = {2},
pages = {85--113},
issn = {0094-9655, 1563-5163},
doi = {10.1080/00949650215733},
urldate = {2024-01-19},
langid = {english},
keywords = {00. Ready for website,00. Unread,01. Goal: inferential or hypothesis testing,02. Causation: cause-and-effect,03. Paper type: guide to use,03. Paper type: math description,03. Paper type: review/comparison/metaanalysis,04. Interpretation and meaning: confidence intervals prediction intervals parameter estimation,04. Interpretation and meaning: diagnostics fitting,04. Interpretation and meaning: multiple comparisons,04. Interpretation and meaning: significance,05. Interpretation and meaning: meeting assumptions,05. Interpretation and meaning: statistical power,09. Study design: repeated measures/randomized block/random effects/correlation structures,10. Study design: interactions,13. Relationship: linear,15. Response variables: multivariate,16. Response variables: frequency,16. Response variables: integers,16. Response variables: interval,16. Response variables: numeric,16. Response variables: ordinal,16. Response variables: ratio,18. Predictor variables: multivariate,18. Predictor variables: univariate,19. Predictor variables: categorical,21. Missing data: none (complete cases) balanced design,21. Missing data: partial cases (pattern - censored),21. Missing data: partial cases (pattern),21. Missing data: partial cases (random cells missing),21. Missing data: whole rows missing / unbalanced design,22. Data assumptions: nonparametric,23. Distributions: simulated randomized computational,25. Domain: QA Mathematics,26. Software: General (applies to any),27. Philosophy: frequentist},
file = {C:\Users\curr0024\Zotero\storage\7A34J8XE\Anderson and Braak - 2003 - Permutation tests for multi-factorial analysis of .pdf}
}
@article{andersonSuggestionsPresentingResults2001,
title = {Suggestions for Presenting the Results of Data Analyses},
author = {Anderson, David R. and Link, William A. and Johnson, Douglas H. and Burnham, Kenneth P.},
year = {2001},
month = jul,
journal = {The Journal of Wildlife Management},
volume = {65},
number = {3},
eprint = {3803088},
eprinttype = {jstor},
pages = {373},
issn = {0022541X},
doi = {10.2307/3803088},
urldate = {2015-10-06},
keywords = {00. Unread,02. Causation: cause-and-effect,03. Paper type: critique,03. Paper type: guide to use,04. Interpretation and meaning: model selection + information theoretic,24. Super helpful,27. Philosophy: frequentist},
file = {C:\Users\curr0024\Zotero\storage\UI8EHE3B\AndersonEtAl2001-presentingdataanalyses.PDF}
}
@misc{anglimBootstrappingBootPackage2009,
title = {Bootstrapping and the Boot Package in {{R}} {\textbar} {{R-bloggers}}},
author = {Anglim, Jeromy},
year = {2009},
month = may,
urldate = {2022-05-20},
abstract = {I was recently asked about options for bootstrapping. The following post sets out some applications of bootstrapping and strategies for implementing it in R.I've found bootstrapping useful in several settings:where the statistic I'm interested in is a ...},
langid = {american},
keywords = {00. Unread,01. Goal: descriptive,01. Goal: inferential or hypothesis testing,02. Causation: association (none implied),02. Causation: cause-and-effect,02. Causation: none (descriptive),03. Paper type: guide to use,04. Interpretation and meaning: confidence intervals prediction intervals parameter estimation,22. Data assumptions: nonparametric,26. Software: R or S Plus,26. Software: SPSS},
file = {C:\Users\curr0024\Zotero\storage\QQPZALU4\bootstrapping-and-the-boot-package-in-r.html}
}
@misc{ANOVAANalysisVAriance,
title = {{{ANOVA}}: {{ANalysis Of VAriance}} between Groups},
urldate = {2022-12-13},
howpublished = {http://www.physics.csbsju.edu/stats/anova.html},
keywords = {01. Goal: inferential or hypothesis testing,02. Causation: cause-and-effect,03. Paper type: guide to use,03. Paper type: math description,04. Interpretation and meaning: significance,13. Relationship: linear,15. Response variables: univariate,16. Response variables: interval,16. Response variables: numeric,16. Response variables: ratio,18. Predictor variables: univariate,19. Predictor variables: categorical,21. Missing data: none (complete cases) balanced design,22. Data assumptions: parametric,23. Distributions: homoscedasticity,23. Distributions: theoretical existing known,25. Domain: A General Works,26. Software: General (applies to any),27. Philosophy: frequentist},
file = {C:\Users\curr0024\Zotero\storage\2XGF8TYB\anova.html}
}
@misc{ANOVATestDefinition,
title = {{{ANOVA Test}}: {{Definition}} \& {{Uses}} ({{Updated}} 2022)},
shorttitle = {{{ANOVA Test}}},
journal = {Qualtrics},
urldate = {2022-09-12},
abstract = {Learn how ANOVA can help you understand your research data, and how to simply set up your very first ANOVA test.},
howpublished = {https://www.qualtrics.com/experience-management/research/anova/},
langid = {english},
keywords = {01. Goal: inferential or hypothesis testing,02. Causation: cause-and-effect,03. Paper type: guide to use,25. Domain: A General Works,26. Software: Qualtrics},
file = {C:\Users\curr0024\Zotero\storage\TPD7HFWZ\anova.html}
}
@incollection{AnswersMostOddNumbered2004,
title = {Answers to {{Most Odd-Numbered Exercises}} and {{All Review Exercises}}},
booktitle = {Statistics for {{Research}}},
year = {2004},
pages = {595--619},
publisher = {John Wiley \& Sons, Ltd},
doi = {10.1002/0471477435.answ1},
urldate = {2023-05-30},
isbn = {978-0-471-47743-3},
langid = {english},
file = {C\:\\Users\\curr0024\\Zotero\\storage\\L65TSP3W\\2004 - Answers to Most Odd-Numbered Exercises and All Rev.pdf;C\:\\Users\\curr0024\\Zotero\\storage\\ASF2JADI\\0471477435.html}
}
@incollection{AppendixUsefulTables2004,
title = {Appendix of {{Useful Tables}}},
booktitle = {Statistics for {{Research}}},
year = {2004},
pages = {511--593},
publisher = {John Wiley \& Sons, Ltd},
doi = {10.1002/0471477435.app1},
urldate = {2023-05-30},
isbn = {978-0-471-47743-3},
langid = {english},
file = {C\:\\Users\\curr0024\\Zotero\\storage\\6PP4TT3A\\2004 - Appendix of Useful Tables.pdf;C\:\\Users\\curr0024\\Zotero\\storage\\89QTWA7H\\0471477435.html}
}
@article{araujoEnsembleForecastingSpecies2007,
title = {Ensemble Forecasting of Species Distributions},
author = {Ara{\'u}jo, M and New, M},
year = {2007},
month = jan,
journal = {Trends in Ecology \& Evolution},
volume = {22},
number = {1},
pages = {42--47},
issn = {01695347},
doi = {10.1016/j.tree.2006.09.010},
urldate = {2017-02-24},
langid = {english},
file = {C:\Users\curr0024\Zotero\storage\JRQJQ978\Araujo and New - 2007 - Ensemble forecasting of species distributions.pdf}
}
@article{araujoFiveChallengesSpecies2006,
title = {Five (or so) Challenges for Species Distribution Modelling},
author = {Ara{\'u}jo, Miguel B. and Guisan, Antoine},
year = {2006},
month = oct,
journal = {Journal of Biogeography},
volume = {33},
number = {10},
pages = {1677--1688},
issn = {0305-0270, 1365-2699},
doi = {10.1111/j.1365-2699.2006.01584.x},
urldate = {2017-03-02},
langid = {english},
file = {C:\Users\curr0024\Zotero\storage\DPJCRC3E\Araujo_Guisan2006JBI.pdf}
}
@article{arlotSurveyCrossvalidationProcedures2010,
title = {A Survey of Cross-Validation Procedures for Model Selection},
author = {Arlot, Sylvain and Celisse, Alain},
year = {2010},
journal = {Statistics Surveys},
volume = {4},
pages = {40--79},
issn = {1935-7516},
doi = {10.1214/09-SS054},
urldate = {2017-02-23},
abstract = {Used to estimate the risk of an estimator or to perform model selection, cross-validation is a widespread strategy because of its simplicity and its (apparent) universality. Many results exist on model selection performances of cross-validation procedures. This survey intends to relate these results to the most recent advances of model selection theory, with a particular emphasis on distinguishing empirical statements from rigorous theoretical results. As a conclusion, guidelines are provided for choosing the best cross-validation procedure according to the particular features of the problem in hand.},
langid = {english},
mrnumber = {MR2602303},
zmnumber = {1190.62080}
}
@book{armitageEncyclopediaBiostatistics2005,
title = {Encyclopedia of Biostatistics},
editor = {Armitage, P. and Colton, Theodore},
year = {2005},
edition = {2nd ed},
publisher = {John Wiley},
address = {Chichester, West Sussex, England ; Hoboken, NJ},
isbn = {978-0-470-84907-1},
lccn = {RA409 .E53 2005},
annotation = {OCLC: ocm57168526}
}
@article{arnoldExploringUseStatistics2022,
title = {Exploring the {{Use}} of {{Statistics Curricula}} with {{Annotated Lesson Notes}}},
author = {Arnold, Elizabeth G. and Green, Jennifer L.},
year = {2022},
month = jul,
journal = {Journal of Statistics and Data Science Education},
volume = {0},
number = {0},
pages = {1--11},
publisher = {Taylor \& Francis},
issn = {null},
doi = {10.1080/26939169.2022.2099486},
urldate = {2022-10-03},
abstract = {In K--12 statistics education, there is a call to integrate statistics content standards throughout a mathematics curriculum and to teach these standards from a data analytic perspective. Annotated lesson notes within a lesson plan are a freely available resource to provide teachers support when navigating potentially unfamiliar statistics content and teaching practices. We identified several types of annotated lesson notes, created two statistics lesson plans that contained various annotated lesson notes, and observed secondary mathematics teachers implement the lesson plans in their intermediate algebra courses. For this study, we qualitatively investigated how two teachers' instructional actions compared to what was prescribed in the annotated lesson notes. We found ways in which the teachers' instructional actions, across their differing contexts, aligned with, varied from, or adapted to the annotated lesson notes. From these results, we highlight affordances and limitations of annotated lesson notes for statistics instruction and offer recommendations for those who create statistics curricula with annotated lesson notes.},
keywords = {00. Software development,24. Super helpful},
file = {C:\Users\curr0024\Zotero\storage\YPYDEM5S\Arnold and Green - 2022 - Exploring the Use of Statistics Curricula with Ann.pdf}
}
@misc{arregoitiaGgplot2VisualizationConditional2019,
type = {Blog},
title = {Ggplot2 Visualization of Conditional Inference Trees},
author = {Arregoitia, Luis D. Verde},
year = {2019},
month = aug,
journal = {Luis D. Verde Arregoitia},
urldate = {2022-06-10},
abstract = {Plotting conditional inference trees with dichotomous responses in R, a grammar of graphics implementation},
langid = {english},
keywords = {04. Interpretation and meaning: exploratory,04. Interpretation and meaning: plotting/visualization,11. Study design: covariates,13. Relationship: linear,13. Relationship: nonlinear,15. Response variables: univariate,16. Response variables: categorical,16. Response variables: numeric,16. Response variables: ordinal,18. Predictor variables: multivariate,19. Predictor variables: categorical,19. Predictor variables: numeric,21. Missing data: none (complete cases) balanced design,21. Missing data: whole rows missing / unbalanced design,22. Data assumptions: nonparametric,23. Distributions: simulated randomized computational,23. Distributions: theoretical existing known,26. Software: R or S Plus,27. Philosophy: frequentist},
file = {C:\Users\curr0024\Zotero\storage\8Z6CZ7CM\plotting-recursive-partitioning-trees.html}
}
@misc{arregoitiaGgplot2VisualizationConditional2019a,
title = {Ggplot2 Visualization of Conditional Inference Trees},
author = {Arregoitia, Luis D. Verde},
year = {2019},
month = aug,
journal = {Luis D. Verde Arregoitia},
urldate = {2024-10-02},
abstract = {Plotting conditional inference trees with dichotomous responses in R, a grammar of graphics implementation},
howpublished = {https://luisdva.github.io/rstats/plotting-recursive-partitioning-trees/},
langid = {english}
}
@misc{associateeditorsofmathematicalreviewsandzbmathMathematicsSubjectClassification2020,
title = {Mathematics {{Subject Classification}}},
author = {{Associate Editors of Mathematical Reviews {and} zbMATH}},
year = {2020},
publisher = {zbMath},
urldate = {2022-12-20},
copyright = {CC-BY-NC-SA},
langid = {english},
file = {C:\Users\curr0024\Zotero\storage\Y54SRNM4\msc2020.pdf}
}
@book{attardSurfacesGaryAttard1998,
title = {Surfaces / {{Gary Attard}}, {{Colin Barnes}}.},
author = {Attard, Gary},
year = {1998},
series = {Oxford Chemistry Primers},
number = {59},
publisher = {Oxford University Press},
address = {Oxford ; New York},
collaborator = {Barnes, Colin},
isbn = {978-0-19-855686-2},
langid = {english},
lccn = {QD 506 .A88 1998},
keywords = {01. Goal: descriptive,01. Goal: inferential or hypothesis testing,01. Goal: predictive or mechanistic,02. Causation: cause-and-effect,03. Paper type: general reference,03. Paper type: math description,04. Interpretation and meaning: effect size variable importance loadings,04. Interpretation and meaning: plotting/visualization,22. Data assumptions: parametric,23. Distributions: simulated randomized computational,24. Super helpful,25. Domain: GE Environmental Sciences,25. Domain: QD Chemistry,25. Domain: QE Geology,25. Domain: QP Physiology,25. Domain: R Medicine (General),25. Domain: TA Engineering (General)}
}
@article{austinSpeciesDistributionModels2007,
title = {Species Distribution Models and Ecological Theory: {{A}} Critical Assessment and Some Possible New Approaches},
shorttitle = {Species Distribution Models and Ecological Theory},
author = {Austin, Mike},
year = {2007},
month = jan,
journal = {Ecological Modelling},
volume = {200},
number = {1-2},
pages = {1--19},
issn = {03043800},
doi = {10.1016/j.ecolmodel.2006.07.005},
urldate = {2017-03-02},
langid = {english},
file = {C:\Users\curr0024\Zotero\storage\FIMZZPCI\Austin 2007.pdf}
}
@misc{awatiGentleIntroductionRandom2016,
title = {A Gentle Introduction to Random Forests Using {{R}}},
author = {Awati, Kailash},
year = {2016},
month = sep,
journal = {Eight to Late},
urldate = {2017-01-13},
abstract = {In a previous post, I described how decision tree algorithms work and demonstrated their use via the rpart library in R. Decision trees work by splitting a dataset recursively. That is, subsets arising from a split are further split until a predetermined termination criterion is reached. At each step, a split is made based on the independent variable that results in the largest possible reduction in heterogeneity of the dependent variable. (Note: readers unfamiliar with decision trees may want to read that post before proceeding) The main drawback of decision trees is that they are prone to overfitting. The reason for this is that trees, if grown deep, are able to fit all kinds of variations in the data, including noise. Although it is possible to address this partially by pruning, the result often remains less than satisfactory. This is because the algorithm makes a locally optimal choice at each split without any regard to whether the choice made is the best one overall. A poor split made in the initial stages can thus doom the model, a problem that cannot be fixed by post-hoc pruning. In this post I describe random forests, a tree-based algorithm that addresses the above shortcoming of decision trees. I'll first describe the intuition behind the algorithm via an analogy and then do a demo using the R randomForest library.},
langid = {english},
keywords = {01. Goal: descriptive,01. Goal: predictive or mechanistic,02. Causation: association (none implied),02. Causation: cause-and-effect,03. Paper type: guide to use,03. Paper type: math description,04. Interpretation and meaning: cross-validation,04. Interpretation and meaning: effect size variable importance loadings,05. Interpretation and meaning: meeting assumptions,10. Study design: interactions,11. Study design: covariates,13. Relationship: linear,13. Relationship: nonlinear,15. Response variables: univariate,16. Response variables: categorical,16. Response variables: numeric,16. Response variables: ordinal,18. Predictor variables: multivariate,19. Predictor variables: categorical,19. Predictor variables: numeric,21. Missing data: none (complete cases) balanced design,21. Missing data: whole rows missing / unbalanced design,22. Data assumptions: nonparametric,23. Distributions: simulated randomized computational,23. Distributions: theoretical existing known,24. Super helpful,26. Software: R or S Plus,Special cases and specific tests: random forest}
}
@article{baileyNewOldMethod1990,
title = {A New, Old Method for Assessing Measurement Error in Both Univariate and Multivariate Morphometric Studies},
shorttitle = {A {{New}}, {{Old Method}} for {{Assessing Measurement Error}} in {{Both Univariate}} and {{Multivariate Morphometric Studies}}},
author = {Bailey, Robert C. and Byrnes, Janice},
year = {1990},
journal = {Systematic Biology},
volume = {39},
number = {2},
pages = {124--130},
doi = {10.2307/2992450},
abstract = {A new approach to assessing percent measurement error (\%ME), using an old statistical technique (Model II Analyses of Variance and Covariance: ANOVA/ANCOVA), was developed using both weight and linear measurements made three times each on 87 freshwater snails (Cipangopaludina chinensis; Vivipariidae). Variability of each measurement, as well as covariability between pairs of measurements, was partitioned into among- and within-snail (i.e., measurement error) components. The \%ME varied by two orders of magnitude across the ten measurement variables considered. Shell weight had the lowest \%ME (0.059\%), while body whorl height had the highest (3.88\%). There was a low correlation between \%ME and the among-snail coefficient of variation for each variable (r = -0.28; P {$>$} 0.20). Within-snail correlations between pairs of measurement variables were uniformly low (rwithin {$\leq$} 0.20), while among-snail correlations were generally very high (r = 0.74 to 0.99) due to the large size range of snails in the sample. In any morphometric study, if \%ME is found to be high for a particular variable, it should either be deleted from the study or remeasured a number of times on every individual to be included in the dataset.},
keywords = {05. Interpretation and meaning: measurement error and repeatability,25. Domain: QH Natural History - Biology,25. Domain: QL Zoology},
file = {C:\Users\curr0024\Zotero\storage\SYVG4ENJ\BaileyByrnes1990-measurementerror-originalpaper.PDF}
}
@article{ballingerUsingGeneralizedEstimating2004,
title = {Using {{Generalized Estimating Equations}} for {{Longitudinal Data Analysis}}},
author = {Ballinger, Gary A.},
year = {2004},
month = apr,
journal = {Organizational Research Methods},
volume = {7},
number = {2},
pages = {127--150},
issn = {10944281, 00000000},
doi = {10.1177/1094428104263672},
urldate = {2015-10-06},
langid = {english},
keywords = {00. Unread,Special cases and specific tests: generalized estimating equations},
file = {C:\Users\curr0024\Zotero\storage\2SYA76WC\Organizational Research Methods-2004-Ballinger-127-50.pdf}
}
@article{bandyopadhyayReviewStatisticalInference2019,
title = {Review of {{{\emph{Statistical Inference}}}}{\emph{ as }}{{{\emph{Severe Testing}}}}{\emph{: }}{{{\emph{How}}}}{\emph{ to }}{{{\emph{Get Beyond}}}}{\emph{ the }}{{{\emph{Statistics Wars}}}}},
shorttitle = {Review of {{{\emph{Statistical Inference}}}}{\emph{ as }}{{{\emph{Severe Testing}}}}},
author = {Bandyopadhyay, Prasanta S.},
year = {2019},
month = may,
issn = {1538-1617},
urldate = {2022-11-17},
collaborator = {Mayo, Deborah G.},
langid = {english},
keywords = {00. Unread,01. Goal: inferential or hypothesis testing,03. Paper type: critique,27. Philosophy: frequentist},
file = {C:\Users\curr0024\Zotero\storage\9JC7AED2\statistical-inference-as-severe-testing-how-to-get-beyond-the-statistics-wars.html}
}
@article{bandyopadhyayReviewStatisticalInference2019a,
title = {Review of {{{\emph{Statistical Inference}}}}{\emph{ as }}{{{\emph{Severe Testing}}}}{\emph{: }}{{{\emph{How}}}}{\emph{ to }}{{{\emph{Get Beyond}}}}{\emph{ the }}{{{\emph{Statistics Wars}}}}},
shorttitle = {Review of {{{\emph{Statistical Inference}}}}{\emph{ as }}{{{\emph{Severe Testing}}}}},
author = {Bandyopadhyay, Prasanta S.},
year = {2019},
month = may,
issn = {1538-1617},
urldate = {2022-06-13},
collaborator = {Mayo, Deborah G.},
langid = {english}
}
@techreport{barangerTutorialPowerAnalyses2022,
type = {Preprint},
title = {Tutorial: {{Power}} Analyses for Interaction Effects in Cross-Sectional Regressions},
shorttitle = {Tutorial},
author = {Baranger, David A and Finsaas, Megan and Goldstein, Brandon and Vize, Colin and Lynam, Donald and Olino, Thomas M},
year = {2022},
month = aug,
institution = {PsyArXiv},
urldate = {2022-09-27},
abstract = {Interaction analyses (also termed `moderation' analyses or `moderated multiple regression') are a form of linear regression analysis designed to test whether the association between two variables changes when conditioned on a third variable. It can be challenging to perform a power analysis for interactions with existing software, particularly when variables are correlated and continuous. Moreover, while power is impacted by variable parameters that are always present in cross-sectional observational studies, such as reliability and skew, it can be unclear how to incorporate these effects into a power analysis. The R package InteractionPoweR and associated Shiny app allow researchers with minimal or no programming experience to perform analytic and simulation-based power analyses for interactions. At minimum, these analyses require the Pearson's correlation between variables and sample size, and parameters including reliability, skew, and the number of discrete levels that a variable takes (e.g., binary or likert scale) can optionally be specified. In this Tutorial we demonstrate how to perform power analyses using our package and give examples of how power can be impacted by main effects, correlations between main effects, reliability, and variable distributions. We close with a brief discussion of how researchers may select an appropriate interaction effect size when performing a power analysis.},
keywords = {00. Unread,01. Goal: inferential or hypothesis testing,02. Causation: association (none implied),03. Paper type: guide to use,04. Interpretation and meaning: effect size variable importance loadings,05. Interpretation and meaning: meeting assumptions,05. Interpretation and meaning: statistical power,10. Study design: interactions,26. Software: RShiny,27. Philosophy: frequentist},
annotation = {https://osf.io/5ptd7},
file = {C:\Users\curr0024\Zotero\storage\D4A5L9AK\Baranger et al. - 2022 - Tutorial Power analyses for interaction effects i.pdf}
}
@article{barrRandomEffectsStructure2013,
title = {Random Effects Structure for Confirmatory Hypothesis Testing: {{Keep}} It Maximal},
shorttitle = {Random Effects Structure for Confirmatory Hypothesis Testing},
author = {Barr, Dale J. and Levy, Roger and Scheepers, Christoph and Tily, Harry J.},
year = {2013},
month = apr,
journal = {Journal of Memory and Language},
volume = {68},
number = {3},
pages = {255--278},
issn = {0749596X},
doi = {10.1016/j.jml.2012.11.001},
urldate = {2022-05-20},
langid = {english},
keywords = {01. Goal: inferential or hypothesis testing,02. Causation: cause-and-effect,03. Paper type: critique,03. Paper type: guide to use,03. Paper type: math description,03. Paper type: review/comparison/metaanalysis,05. Interpretation and meaning: meeting assumptions,05. Interpretation and meaning: statistical power,09. Study design: repeated measures/randomized block/random effects/correlation structures,15. Response variables: univariate,18. Predictor variables: multivariate,21. Missing data: none (complete cases) balanced design,21. Missing data: partial cases (random cells missing),21. Missing data: whole rows missing / unbalanced design,22. Data assumptions: parametric,25. Domain: B Philosophy. Psychology. Religion,25. Domain: BF Psychology,25. Domain: P Philology. Linguistics},
file = {C:\Users\curr0024\Zotero\storage\JEN4GK7Y\Barr et al. - 2013 - Random effects structure for confirmatory hypothes.pdf}
}
@article{bartkoCorrectiveNoteIntraclass1974,
title = {Corrective Note to: "{{The}} Intraclass Correlation Coefficient as a Measure of Reliability."},
shorttitle = {Corrective Note To},
author = {Bartko, John J.},
year = {1974},
journal = {Psychological Reports},
volume = {34},
number = {2},
pages = {418--418},
publisher = {Psychological Reports},
address = {US},
issn = {1558-691X},
doi = {10.2466/pr0.1974.34.2.418},
abstract = {Presents corrected equations for the author's previous paper (see record 1966-11636-001) which suggested a procedure for estimating the reliability of sets of ratings in terms of intraclass correlation coefficients. (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
keywords = {Unread},
file = {C:\Users\curr0024\Zotero\storage\5U8IZWS3\1974-31239-001.html}
}
@misc{BasicsEstimatedMarginal,
title = {Basics of Estimated Marginal Means (Emmeans Package, {{Version}} 1.8.1.1)},
urldate = {2022-09-14},
abstract = {Index of all vignette topics},
keywords = {24. Super helpful}
}
@article{batesFittingLinearMixed2005,
title = {Fitting Linear Mixed Models in {{R}}},
author = {Bates, Douglas},
year = {2005},
journal = {R News},
volume = {5},
number = {1},
pages = {27--30},
issn = {1609-3631},
urldate = {2015-10-01},
keywords = {02. Causation: cause-and-effect,03. Paper type: guide to use,09. Study design: repeated measures/randomized block/random effects/correlation structures,13. Relationship: linear,15. Response variables: univariate,16. Response variables: numeric,18. Predictor variables: multivariate,19. Predictor variables: categorical,19. Predictor variables: numeric,22. Data assumptions: parametric,23. Distributions: theoretical existing known,26. Software: R or S Plus,27. Philosophy: frequentist},
file = {C:\Users\curr0024\Zotero\storage\RDFBTGH5\Rnews_2005-1_intro_to_lme4_mixedmodelpackage.pdf}
}
@article{batesFittingLinearMixedEffects2015,
title = {Fitting {{Linear Mixed-Effects Models Using}} {\textbf{Lme4}}},
author = {Bates, Douglas and M{\"a}chler, Martin and Bolker, Ben and Walker, Steve},
year = {2015},
journal = {Journal of Statistical Software},
volume = {67},
number = {1},
pages = {1--48},
issn = {1548-7660},
doi = {10.18637/jss.v067.i01},
urldate = {2022-05-20},
langid = {english},
keywords = {01. Goal: inferential or hypothesis testing,02. Causation: cause-and-effect,03. Paper type: critique,03. Paper type: guide to use,03. Paper type: math description,04. Interpretation and meaning: confidence intervals prediction intervals parameter estimation,04. Interpretation and meaning: diagnostics fitting,04. Interpretation and meaning: plotting/visualization,04. Interpretation and meaning: significance,05. Interpretation and meaning: meeting assumptions,09. Study design: repeated measures/randomized block/random effects/correlation structures,11. Study design: covariates,13. Relationship: linear,21. Missing data: none (complete cases) balanced design,21. Missing data: whole rows missing / unbalanced design,22. Data assumptions: parametric,23. Distributions: homoscedasticity,24. Super helpful,26. Software: R or S Plus,27. Philosophy: evidential statistics},
file = {C:\Users\curr0024\Zotero\storage\T4UW47IG\Bates et al. - 2015 - Fitting Linear Mixed-Effects Models Using lme4.pdf}
}
@article{batesLmerSASPROC2009,
title = {Lmer for {{SAS PROC MIXED Users}}},
author = {Bates, D.},
year = {2009},
urldate = {2012-10-13},
keywords = {01. Goal: inferential or hypothesis testing,02. Causation: cause-and-effect,03. Paper type: guide to use,03. Paper type: review/comparison/metaanalysis,09. Study design: repeated measures/randomized block/random effects/correlation structures,15. Response variables: univariate,16. Response variables: numeric,26. Software: R or S Plus,26. Software: SAS,27. Philosophy: evidential statistics,27. Philosophy: frequentist},
file = {C\:\\Users\\curr0024\\Zotero\\storage\\465Z8Y6I\\Usinglmer_forSASusers_bates.pdf;C\:\\Users\\curr0024\\Zotero\\storage\\BHYGYN6G\\Usinglmer_SASmixed_package.pdf}
}
@article{BayesianModelAveraging,
title = {Bayesian Model Averaging},
urldate = {2017-01-13},
file = {C:\Users\curr0024\Zotero\storage\UZGUCY68\ModelAve.pdf}
}
@article{beachyEffectPredatoryLarval1997,
title = {Effect of {{Predatory Larval Desmognathus}} Quadramaculatus on {{Growth}}, {{Survival}}, and {{Metamorphosis}} of {{Larval Eurycea}} Wilderae},
author = {Beachy, Christopher King},
year = {1997},
month = feb,
journal = {Copeia},
volume = {1997},
number = {1},
eprint = {1447848},
eprinttype = {jstor},
pages = {131},
issn = {00458511},
doi = {10.2307/1447848},
urldate = {2020-11-20},
langid = {english},
keywords = {03. Paper type: good example,15. Response variables: multivariate,16. Response variables: numeric,18. Predictor variables: multivariate,19. Predictor variables: categorical,21. Missing data: none (complete cases) balanced design,22. Data assumptions: parametric,23. Distributions: homoscedasticity,23. Distributions: theoretical existing known},
file = {C:\Users\curr0024\Zotero\storage\IQJBCRXA\Beachy - 1997 - Effect of Predatory Larval Desmognathus quadramacu.pdf}
}
@article{bealeRegressionAnalysisSpatial2010,
title = {Regression Analysis of Spatial Data},
author = {Beale, Colin M. and Lennon, Jack J. and Yearsley, Jon M. and Brewer, Mark J. and Elston, David A.},
year = {2010},
month = feb,
journal = {Ecology Letters},
volume = {13},
number = {2},
pages = {246--264},
issn = {1461023X, 14610248},
doi = {10.1111/j.1461-0248.2009.01422.x},
urldate = {2012-10-13}
}
@article{beasleyMultipleRegressionApproach1995,
title = {Multiple {{Regression Approach}} to {{Analyzing Contingency Tables}}: {{Post Hoc}} and {{Planned Comparison Procedures}}},
shorttitle = {Multiple {{Regression Approach}} to {{Analyzing Contingency Tables}}},
author = {Beasley, T. Mark and Schumacker, Randall E.},
year = {1995},
month = oct,
journal = {The Journal of Experimental Education},
volume = {64},
number = {1},
pages = {79--93},
issn = {0022-0973, 1940-0683},
doi = {10.1080/00220973.1995.9943797},
urldate = {2022-06-10},
abstract = {Post hoc and planned comparison procedures for interpreting chi square contingency-table test results, not currently discussed in most standard text books, are presented. A planned comparison procedure that simplifies the tedious process of partitioning a contingency table by creating single-degree-of-freedom con trasts through a regression-based approach is proposed. Importantly, these post hoc methods supplement the analysis of standardized residuals by reporting the per centage contribution for each cell to the overall chi-square statistic (relative contri bution) and to the percentage of variance shared by the two factors (absolute contri bution). Both methods can be readily incorporated into existing statistical packages such as SAS or SPSS. The equivalence of the percentage contribution method to the more common standardized residual method is also presented along with an exam ple of a typical application.},
langid = {english},
keywords = {00. Unread},
file = {C:\Users\curr0024\Zotero\storage\82DFBBBX\Beasley and Schumacker - 1995 - Multiple Regression Approach to Analyzing Continge.pdf}
}
@misc{BeginnerGuideMarginal,
title = {A {{Beginner}}'s {{Guide}} to {{Marginal Effects}} {\textbar} {{University}} of {{Virginia Library Research Data Services}} + {{Sciences}}},
urldate = {2022-09-29},
howpublished = {https://data.library.virginia.edu/a-beginners-guide-to-marginal-effects/},
file = {C:\Users\curr0024\Zotero\storage\PNLII5TP\a-beginners-guide-to-marginal-effects.html}
}
@misc{behacadGeneralLinearModel2020,
type = {Forum Post},
title = {General {{Linear Model}} vs. {{Generalized Linear Model}} (with an Identity Link Function?)},
shorttitle = {General {{Linear Model}} vs. {{Generalized Linear Model}} (with an Identity Link Function?},
author = {Behacad},
year = {2020},
month = jun,
journal = {Cross Validated},
urldate = {2022-12-03},
keywords = {01. Goal: inferential or hypothesis testing,02. Causation: cause-and-effect,03. Paper type: guide to use,24. Super helpful,27. Philosophy: frequentist}
}
@article{bellSmallSampleEstimation2011,
title = {Small Sample Estimation Properties of Longitudinal Count Models},
author = {Bell, Melanie L. and Grunwald, Gary K.},
year = {2011},
month = sep,
journal = {Journal of Statistical Computation and Simulation},
volume = {81},
number = {9},
pages = {1067--1079},
issn = {0094-9655, 1563-5163},
doi = {10.1080/00949651003674144},
urldate = {2022-05-20},
langid = {english}
}
@article{bellTheseusExpertStatistical1989,
title = {Theseus: {{An Expert Statistical Consultant}}},
shorttitle = {Theseus},
author = {Bell, Edwina and Watts, Peter and Alexander, John},
year = {1989},
month = feb,
journal = {American Journal of Mathematical and Management Sciences},
volume = {9},
number = {3-4},
pages = {361--370},
issn = {0196-6324, 2325-8454},
doi = {10.1080/01966324.1989.10737269},
urldate = {2020-12-09},
langid = {english},
keywords = {00. For my literature review},
file = {C:\Users\curr0024\Zotero\storage\HXHJK2LE\Bell et al. - 1989 - Theseus An Expert Statistical Consultant.pdf}
}
@article{ben-saidSpatialPointpatternAnalysis2021,
title = {Spatial Point-Pattern Analysis as a Powerful Tool in Identifying Pattern-Process Relationships in Plant Ecology: An Updated Review},
shorttitle = {Spatial Point-Pattern Analysis as a Powerful Tool in Identifying Pattern-Process Relationships in Plant Ecology},
author = {{Ben-Said}, Mariem},
year = {2021},
month = aug,
journal = {Ecological Processes},
volume = {10},
number = {1},
pages = {56},
issn = {2192-1709},
doi = {10.1186/s13717-021-00314-4},
urldate = {2022-11-18},
abstract = {Ecological processes such as seedling establishment, biotic interactions, and mortality can leave footprints on species spatial structure that can be detectable through spatial point-pattern analysis (SPPA). Being widely used in plant ecology, SPPA is increasingly carried out to describe biotic interactions and interpret pattern-process relationships. However, some aspects are still subjected to a non-negligible debate such as required sample size (in terms of the number of points and plot area), the link between the low number of points and frequently observed random (or independent) patterns, and relating patterns to processes. In this paper, an overview of SPPA is given based on rich and updated literature providing guidance for ecologists (especially beginners) on summary statistics, uni-/bi-/multivariate analysis, unmarked/marked analysis, types of marks, etc. Some ambiguities in SPPA are also discussed.},
keywords = {00. Unread,01. Goal: inferential or hypothesis testing,02. Causation: cause-and-effect,03. Paper type: critique,03. Paper type: general reference,03. Paper type: guide to use,03. Paper type: review/comparison/metaanalysis},
file = {C\:\\Users\\curr0024\\Zotero\\storage\\MDNMWPBG\\Ben-Said - 2021 - Spatial point-pattern analysis as a powerful tool .pdf;C\:\\Users\\curr0024\\Zotero\\storage\\UGEMJB97\\s13717-021-00314-4.html}
}
@misc{berkeleydatascienceeducationprogramdsepTextbooksComputingData,
title = {Textbooks for {{Computing}}, {{Data Science}}, and {{Society}} at {{Berkley}}},
author = {Berkeley Data Science Education Program (DSEP)},
urldate = {2023-01-27},
abstract = {Berkeley's core data science classes all offer its students free access to an interactive online textbook. The books are created using a service from Project Jupyter in collaboration with the Berkeley Data Science Education Program (DSEP) known as a Jupyter Book. The most updated textbooks for these core classes are listed below.},
howpublished = {https://data.berkeley.edu/academics/campus-resources/textbooks},
keywords = {00. Unread,03. Paper type: general reference,03. Paper type: guide to use,26. Software: General (applies to any),26. Software: Python},
file = {C:\Users\curr0024\Zotero\storage\TTPYI7P3\textbooks.html}
}
@article{berkWhatYouCan2010,
title = {What {{You Can}} and {{Can}}'t {{Properly Do}} with {{Regression}}},
author = {Berk, Richard},
year = {2010},
month = dec,
journal = {Journal of Quantitative Criminology},
volume = {26},
number = {4},
pages = {481--487},
issn = {0748-4518, 1573-7799},
doi = {10.1007/s10940-010-9116-4},
urldate = {2023-08-21},
langid = {english},
keywords = {00. Unread,01. Goal: inferential or hypothesis testing,02. Causation: cause-and-effect,03. Paper type: review/comparison/metaanalysis},
file = {C:\Users\curr0024\Zotero\storage\QMY57Y6N\Berk - 2010 - What You Can and Can’t Properly Do with Regression.pdf}
}
@article{bernerCorrectionBootstrapApproach2009,
title = {Correction of a Bootstrap Approach to Testing for Evolution along Lines of Least Resistance},
author = {Berner, D.},
year = {2009},
month = dec,
journal = {Journal of Evolutionary Biology},
volume = {22},
number = {12},
pages = {2563--2565},
issn = {1010061X, 14209101},
doi = {10.1111/j.1420-9101.2009.01869.x},
urldate = {2012-10-13}
}
@misc{bevansChoosingRightStatistical2020,
title = {Choosing the {{Right Statistical Test}} {\textbar} {{Types}} \& {{Examples}}},
author = {Bevans, Rebecca},
year = {2020},
month = jan,
journal = {Scribbr},
urldate = {2022-12-09},
abstract = {Statistical tests are used in hypothesis testing. They can be used to: determine whether a predictor variable has a statistically significant relationship},
howpublished = {https://www.scribbr.com/statistics/statistical-tests/},
langid = {american},
file = {C:\Users\curr0024\Zotero\storage\FJAWFWMQ\statistical-tests.html}
}
@article{biauRandomForestGuided2016,
title = {A Random Forest Guided Tour},
author = {Biau, G{\'e}rard and Scornet, Erwan},
year = {2016},
journal = {TEST},
volume = {25},
number = {2},
pages = {197--227},
doi = {10.1007/s11749-016-0481-7},
urldate = {2017-02-27},
keywords = {03. Paper type: math description},
file = {C\:\\Users\\curr0024\\Zotero\\storage\\PX3CZGU7\\Biau and Scornet - 2016 - A random forest guided tour.pdf;C\:\\Users\\curr0024\\Zotero\\storage\\ZM4SFWRE\\bbdf0f8620ee5c623048f421f6a439d5fa30.pdf}
}
@incollection{BinomialDistributions2004,
title = {Binomial {{Distributions}}},
booktitle = {Statistics for {{Research}}},
year = {2004},
pages = {49--80},
publisher = {John Wiley \& Sons, Ltd},
doi = {10.1002/0471477435.ch3},
urldate = {2023-05-30},
abstract = {This chapter contains sections titled: The Nature of Binomial Distributions Testing Hypotheses Estimation Nonparametric Statistics: Median Test Review Exercises Selected Readings},
chapter = {3},
isbn = {978-0-471-47743-3},
langid = {english},
file = {C\:\\Users\\curr0024\\Zotero\\storage\\R9F9UE2C\\2004 - Binomial Distributions.pdf;C\:\\Users\\curr0024\\Zotero\\storage\\FVUMCVBQ\\0471477435.html}
}
@article{bockenholtKnowledgebasedSystemSupporting1989,
title = {A Knowledge-Based System for Supporting Data Analysis Problems},
author = {B{\"o}ckenholt, I and Both, M and Gaul, W},
year = {1989},
month = dec,
journal = {Decision Support Systems},
volume = {5},
number = {4},
pages = {345--354},
issn = {01679236},
doi = {10.1016/0167-9236(89)90014-6},
urldate = {2020-12-09},
langid = {english},
keywords = {00. For my literature review},
file = {C:\Users\curr0024\Zotero\storage\5XSUK5RQ\Böckenholt et al. - 1989 - A knowledge-based system for supporting data analy.pdf}
}
@misc{bolkerAnswerAreDegrees2014,
title = {Answer to "{{Are}} Degrees of Freedom in {{lmerTest}}::Anova Correct? {{They}} Are Very Different from {{RM-ANOVA}}"},
shorttitle = {Answer to "{{Are}} Degrees of Freedom in {{lmerTest}}},
author = {Bolker, Ben},
year = {2014},
month = feb,
journal = {Cross Validated},
urldate = {2022-12-12},
keywords = {01. Goal: inferential or hypothesis testing,02. Causation: cause-and-effect,03. Paper type: guide to use,04. Interpretation and meaning: confidence intervals prediction intervals parameter estimation,04. Interpretation and meaning: significance,05. Interpretation and meaning: meeting assumptions,09. Study design: repeated measures/randomized block/random effects/correlation structures,10. Study design: interactions,13. Relationship: linear,15. Response variables: univariate,16. Response variables: interval,16. Response variables: numeric,16. Response variables: ratio,18. Predictor variables: multivariate,18. Predictor variables: univariate,19. Predictor variables: categorical,21. Missing data: none (complete cases) balanced design,21. Missing data: whole rows missing / unbalanced design,22. Data assumptions: parametric,23. Distributions: homoscedasticity,23. Distributions: theoretical existing known,25. Domain: A General Works,26. Software: R or S Plus,27. Philosophy: frequentist},
file = {C:\Users\curr0024\Zotero\storage\276D2D8G\are-degrees-of-freedom-in-lmertestanova-correct-they-are-very-different-from.html}
}
@misc{bolkerAnswerMixedEffects2016,
title = {Answer to "{{Mixed}} Effects Model Error Message: {{Model}} Is Nearly Unidentifiable: Large Eigenvalue Ratio"},
shorttitle = {Answer to "{{Mixed}} Effects Model Error Message},
author = {Bolker, Ben},
year = {2016},
month = aug,
journal = {Cross Validated},
urldate = {2022-05-20},
langid = {english},
keywords = {01. Goal: inferential or hypothesis testing,02. Causation: cause-and-effect,03. Paper type: guide to use,09. Study design: repeated measures/randomized block/random effects/correlation structures,24. Super helpful,26. Software: R or S Plus},
file = {C:\Users\curr0024\Zotero\storage\GAJWUK6V\mixed-effects-model-error-message-model-is-nearly-unidentifiable-large-eigenva.html}
}
@book{bolkerEcologicalModelsData2008,
title = {Ecological Models and Data in {{R}}},
author = {Bolker, Benjamin M.},
year = {2008},
publisher = {Princeton University Press},
address = {Princeton, N.J},
isbn = {978-0-691-12522-0},
lccn = {QH541.15.S72 B65 2008},
keywords = {00. Unread,01. Goal: inferential or hypothesis testing,03. Paper type: general reference,03. Paper type: good example,03. Paper type: guide to use,03. Paper type: math description,04. Interpretation and meaning: confidence intervals prediction intervals parameter estimation,04. Interpretation and meaning: exploratory,04. Interpretation and meaning: plotting/visualization,04. Interpretation and meaning: significance,05. Interpretation and meaning: meeting assumptions,08. Study design: sample size,10. Study design: interactions,22. Data assumptions: parametric,23. Distributions: homoscedasticity,23. Distributions: simulated randomized computational,23. Distributions: theoretical existing known,24. Super helpful,25. Domain: Q Science (General),25. Domain: QH Natural History - Biology,25. Domain: QK Botany,25. Domain: QL Zoology,25. Domain: QP Physiology,25. Domain: QR Microbiology,25. Domain: S Agriculture (General)},
file = {C:\Users\curr0024\Zotero\storage\JMAC3F6L\index.html}
}
@book{bolkerEcologicalModelsData2008a,
title = {Ecological Models and Data in {{R}}},
author = {Bolker, Benjamin M.},
year = {2008},
publisher = {Princeton University Press},
address = {Princeton},
file = {C:\Users\curr0024\Zotero\storage\2F4ICTQR\Bolker 2008 - Ecological Models and Data in R.pdf}
}
@article{bolkerGeneralizedLinearMixed2009,
title = {Generalized Linear Mixed Models: A Practical Guide for Ecology and Evolution},
shorttitle = {Generalized Linear Mixed Models},
author = {Bolker, Benjamin M. and Brooks, Mollie E. and Clark, Connie J. and Geange, Shane W. and Poulsen, John R. and Stevens, M. Henry H. and White, Jada-Simone S.},
year = {2009},
month = mar,
journal = {Trends in Ecology \& Evolution},
volume = {24},
number = {3},
pages = {127--135},
issn = {0169-5347},
doi = {10.1016/j.tree.2008.10.008},
urldate = {2022-01-24},
abstract = {How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize `best-practice' data analysis procedures for scientists facing this challenge.},
langid = {english},
keywords = {02. Causation: cause-and-effect,03. Paper type: guide to use,09. Study design: repeated measures/randomized block/random effects/correlation structures,24. Super helpful,25. Domain: QH Natural History - Biology,27. Philosophy: frequentist},
file = {C:\Users\curr0024\Zotero\storage\PMNCMU8G\1-s2.0-S0169534709000196-main.pdf}
}
@misc{bolkerGLMMFAQ2022,
type = {{{FAQ}}},
title = {{{GLMM FAQ}}},
author = {Bolker, Ben},
year = {2022},
month = mar,
journal = {r-sig-mixed-models Mailing List FAQ},
urldate = {2022-01-24},
abstract = {This is an informal FAQ list for the r-sig-mixed-models mailing list.},
howpublished = {https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html},
langid = {english},
keywords = {01. Goal: inferential or hypothesis testing,02. Causation: cause-and-effect,03. Paper type: guide to use,04. Interpretation and meaning: confidence intervals prediction intervals parameter estimation,04. Interpretation and meaning: diagnostics fitting,04. Interpretation and meaning: effect size variable importance loadings,04. Interpretation and meaning: multiple comparisons,04. Interpretation and meaning: plotting/visualization,04. Interpretation and meaning: significance,05. Interpretation and meaning: meeting assumptions,05. Interpretation and meaning: statistical power,05. Interpretation and meaning: survey/sampling bias,08. Study design: sample size,09. Study design: repeated measures/randomized block/random effects/correlation structures,10. Study design: interactions,11. Study design: covariates,13. Relationship: linear,13. Relationship: nonlinear,15. Response variables: univariate,16. Response variables: frequency,16. Response variables: integers,16. Response variables: interval,16. Response variables: ordinal,16. Response variables: ratio,18. Predictor variables: multivariate,19. Predictor variables: categorical,19. Predictor variables: numeric,20. Predictor variables: interval,20. Predictor variables: ratio,21. Missing data: none (complete cases) balanced design,21. Missing data: partial cases (pattern),21. Missing data: partial cases (random cells missing),21. Missing data: whole rows missing / unbalanced design,23. Distributions: homoscedasticity,23. Distributions: simulated randomized computational,23. Distributions: theoretical existing known,24. Super helpful,25. Domain: A General Works,25. Domain: QA Mathematics,26. Software: R or S Plus,27. Philosophy: Bayesian,27. Philosophy: frequentist},