bib.bib

@article{belloInvitedReviewReproducible2018,
  title = {Invited Review: {{Reproducible}} Research from Noisy Data: {{Revisiting}} Key Statistical Principles for the Animal Sciences},
  shorttitle = {Invited Review},
  author = {Bello, Nora M. and Renter, David G.},
  year = {2018},
  month = jul,
  volume = {101},
  pages = {5679--5701},
  issn = {0022-0302},
  doi = {10.3168/jds.2017-13978},
  url = {http://www.sciencedirect.com/science/article/pii/S002203021830403X},
  urldate = {2020-08-09},
  abstract = {Reproducible results define the very core of scientific integrity in modern research. Yet, legitimate concerns have been raised about the reproducibility of research findings, with important implications for the advancement of science and for public support. With statistical practice increasingly becoming an essential component of research efforts across the sciences, this review article highlights the compelling role of statistics in ensuring that research findings in the animal sciences are reproducible\textemdash in other words, able to withstand close interrogation and independent validation. Statistics set a formal framework and a practical toolbox that, when properly implemented, can recover signal from noisy data. Yet, misconceptions and misuse of statistics are recognized as top contributing factors to the reproducibility crisis. In this article, we revisit foundational statistical concepts relevant to reproducible research in the context of the animal sciences, raise awareness on common statistical misuse undermining it, and outline recommendations for statistical practice. Specifically, we emphasize a keen understanding of the data generation process throughout the research endeavor, from thoughtful experimental design and randomization, through rigorous data analysis and inference, to careful wording in communicating research results to peer scientists and society in general. We provide a detailed discussion of core concepts in experimental design, including data architecture, experimental replication, and subsampling, and elaborate on practical implications for proper elicitation of the scope of reach of research findings. For data analysis, we emphasize proper implementation of mixed models, in terms of both distributional assumptions and specification of fixed and random effects to explicitly recognize multilevel data architecture. This is critical to ensure that experimental error for treatments of interest is properly recognized and inference is correctly calibrated. Inferential misinterpretations associated with use of P-values, both significant and not, are clarified, and problems associated with error inflation due to multiple comparisons and selective reporting are illustrated. Overall, we advocate for a responsible practice of statistics in the animal sciences, with an emphasis on continuing quantitative education and interdisciplinary collaboration between animal scientists and statisticians to maximize reproducibility of research findings.},
  file = {/home/sasha/Zotero/storage/NGFNGFLS/S002203021830403X.html},
  journal = {Journal of Dairy Science},
  keywords = {R workbook},
  language = {en},
  number = {7}
}

@book{dalgaardIntroductoryStatistics2008,
  title = {Introductory {{Statistics}} with {{R}}},
  author = {Dalgaard, Peter},
  year = {2008},
  month = aug,
  publisher = {{Springer}},
  abstract = {R is an Open Source implementation of the S language. It works on multiple computing platforms and can be freely downloaded. R is now in widespread use for teaching at many levels as well as for practical data analysis and methodological development. This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets. The statistical methodology includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last six chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression, and nonlinear regression. In the second edition, the text and code have been updated to R version 2.6.2. The last two methodological chapters are new, as is a chapter on advanced data handling. The introductory chapter has been extended and reorganized as two chapters. Exercises have been revised and answers are now provided in an Appendix. Peter Dalgaard is associate professor at the Department of Biostatistics at the University of Copenhagen and has extensive experience in teaching within the PhD curriculum at the Faculty of Health Sciences. He has been a member of the R Core Team since 1997.},
  isbn = {978-0-387-79053-4},
  keywords = {Business \& Economics / Statistics,Computers / Bioinformatics,Computers / Computerized Home \& Entertainment,Computers / Mathematical \& Statistical Software,Computers / Programming Languages / General,Mathematics / Probability \& Statistics / General,R workbook,Science / Life Sciences / Anatomy \& Physiology,Science / Life Sciences / Biology,Science / Life Sciences / Molecular Biology},
  language = {en}
}

@article{devlamynckEffectGrowthMedium2020,
  title = {Effect of the Growth Medium Composition on Nitrate Accumulation in the Novel Protein Crop {{Lemna}} Minor},
  author = {Devlamynck, Reindert and {Fernandes de Souza}, Marcella and Bog, Manuela and Leenknegt, Jan and Eeckhout, Mia and Meers, Erik},
  year = {2020},
  month = dec,
  volume = {206},
  pages = {111380},
  issn = {0147-6513},
  doi = {10.1016/j.ecoenv.2020.111380},
  url = {https://www.sciencedirect.com/science/article/pii/S0147651320312173},
  urldate = {2021-05-14},
  abstract = {Duckweed is a potential alternative protein source for food and feed. However, little is known about the nitrate accumulation in this plant. A high nitrate level in vegetables can indirectly lead to an elevated intake of nitrites and N-nitroso compounds, increasing the risk of diseases for humans and animals. This research hypothesizes that the nitrate accumulation of Lemna minor differs between growing media. Additionally, it evaluates whether legal safety levels of nitrate for human and animal intake are exceeded. The duckweed was grown on (i) rainwater, and (ii) three synthetic media containing different nutrient levels. Furthermore, (iii) biological effluent of swine manure treatment and (iv) aquaculture effluent from pikeperch production were used, as these are potential media for closing nutrient loops in the agriculture sector. It was found that nitrate levels increased with the increasing availability of macronutrients in the water, and pH showed a particularly strong negative correlation with the nitrate levels in the plant. Nevertheless, nitrate content never exceeded 530~mg NO3 kg-1 fresh weight. To conclude, Lemna minor's nitrate content was below safety limits for human consumption in all tested growing media; however, a potential risk for ruminants was observed as these are more sensitive to nitrate conversions in their gastro-intestinal track.},
  file = {/home/sasha/Zotero/storage/YDLGNZ45/S0147651320312173.html},
  journal = {Ecotoxicology and Environmental Safety},
  keywords = {R workbook},
  language = {en}
}

@article{devlamynckNitrateAccumulationLemna2020,
  title = {Nitrate Accumulation in Lemna Minor on 6 Different Media},
  author = {Devlamynck, Reindert},
  year = {2020},
  month = oct,
  volume = {1},
  publisher = {{Mendeley}},
  doi = {10.17632/f5t9d2fp8y.1},
  url = {https://data.mendeley.com/datasets/f5t9d2fp8y/1},
  urldate = {2021-05-14},
  abstract = {Duckweed is a potential alternative protein source for food and feed. However, little is known about the nitrate accumulation in this plant. A high nitrate level in vegetables can indirectly lead to an elevated intake of nitrites and N-nitroso compounds, increasing the risk of diseases for humans and animals. This research evaluated whether duckweed grown on different media accumulates nitrate beyond legal safety levels for human and animal intake. Duckweed was grown on (i) rainwater, and (ii) three synthetic media containing different nutrient levels. Furthermore, (iii) biological effluent of swine manure treatment and (iv) aquaculture effluent from pikeperch production were used, as these are potential media for closing nutrient loops in the agriculture sector. It was found that nitrate levels increased with the increasing availability of macronutrients in the water, and pH showed a particularly strong negative correlation with the nitrate levels in duckweed. Nevertheless, nitrate content of duckweed never exceeded 530 mg NO3 kg-1 fresh weight. To conclude, duckweed's nitrate content was below safety limits for human consumption in all tested growing media; however, a potential risk for ruminants was observed as these are more sensitive to nitrate conversions in their gastro-intestinal track. The first tab contains information on the units and the measured parameters in both the water and duckweed.},
  file = {/home/sasha/Zotero/storage/ND89QW49/1.html},
  keywords = {R workbook},
  language = {en}
}

@article{diwakarEvaluationNormalLognormal2017,
  title = {An Evaluation of Normal versus Lognormal Distribution in Data Description and Empirical Analysis},
  author = {Diwakar, Rekha},
  year = {2017},
  month = dec,
  volume = {22},
  pages = {1--15},
  issn = {1531-7714},
  url = {http://pareonline.net/getvn.asp?v=22&n=13},
  urldate = {2019-08-12},
  abstract = {Many existing methods of statistical inference and analysis rely heavily on the assumption that the data are normally distributed. However, the normality assumption is not fulfilled when dealing with data which does not contain negative values or are otherwise skewed \textendash{} a common occurrence in diverse disciplines such as finance, economics, political science, sociology, philology, biology and physical and industrial processes. In this situation, a lognormal distribution may better represent the data than the normal distribution. In this paper, I re-visit the key attributes of the normal and lognormal distributions, and demonstrate through an empirical analysis of the `number of political parties' in India, how logarithmic transformation can help in bringing a lognormally distributed data closer to a normal one. The paper also provides further empirical evidence to show that many variables of interest to political and other social scientists could be better modelled using the lognormal distribution. More generally, the paper emphasises the potential for improved description and empirical analysis of quantitative data by paying more attention to its distribution, and complements previous publications in Practical Research and Assessment Evaluation (PARE) on this subject.},
  copyright = {arr},
  journal = {Practical Assessment, Research \& Evaluation},
  keywords = {R workbook,Statistical methods},
  language = {en}
}

@book{farawayExtendingLinearModel2006,
  title = {Extending the {{Linear Model}} with {{R}}: {{Generalized Linear}}, {{Mixed Effects}} and {{Nonparametric Regression Models}}},
  shorttitle = {Extending the {{Linear Model}} with {{R}}},
  author = {Faraway, Julian J.},
  year = {2006},
  edition = {First},
  publisher = {{Chapman and Hall/CRC}},
  isbn = {1-58488-424-X},
  keywords = {R,R workbook}
}

@book{farawayLinearModels2005,
  title = {Linear {{Models}} with {{R}}},
  author = {Faraway, Julian James},
  year = {2005},
  publisher = {{Chapman \& Hall/CRC}},
  address = {{Boca Raton}},
  isbn = {1-58488-425-8},
  keywords = {R,R workbook},
  lccn = {QA279 .F37 2005},
  number = {v. 63},
  series = {Texts in Statistical Science}
}

@book{farawayLinearModelsPython2020,
  title = {Linear {{Models}} with {{Python}}},
  author = {Faraway, Julian J.},
  year = {2020},
  month = dec,
  edition = {1st edition},
  publisher = {{Chapman and Hall/CRC}},
  address = {{Boca Raton}},
  abstract = {Praise for Linear Models with R: This book is a must-have tool for anyone interested in understanding and applying linear models. The logical ordering of the chapters is well thought out and portrays Faraway's wealth of experience in teaching and using linear models. \ldots{} It lays down the material in a logical and intricate manner and makes linear modeling appealing to researchers from virtually all fields of study. -Biometrical Journal Throughout, it gives plenty of insight \ldots{} with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice, well epitomized with the examples chosen\ldots I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models. -Journal of the Royal Statistical Society Like its widely praised, best-selling companion version, Linear Models with R, this book replaces R with Python to seamlessly give a coherent exposition of the practice of linear modeling. Linear Models with Python offers up-to-date insight on essential data analysis topics, from estimation, inference and prediction to missing data, factorial models and block designs. Numerous examples illustrate how to apply the different methods using Python. Features:   Python is a powerful, open source programming language increasingly being used in data science, machine learning and computer science. Python and R are similar, but R was designed for statistics, while Python is multi-talented.   This version replaces R with Python to make it accessible to a greater number of users outside of statistics, including those from Machine Learning.   A reader coming to this book from an ML background will learn new statistical perspectives on learning from data.   Topics include Model Selection, Shrinkage, Experiments with Blocks and Missing Data.   Includes an Appendix on Python for beginners.   Linear Models with Python explains how to use linear models in physical science, engineering, social science and business applications. It is ideal as a textbook for linear models or linear regression courses.},
  isbn = {978-1-138-48395-8},
  keywords = {R workbook},
  language = {English}
}

@article{garcia-avilaTreatmentMunicipalWastewater2020,
  title = {Treatment of Municipal Wastewater by Vertical Subsurface Flow Constructed Wetland: {{Data}} Collection on Removal Efficiency Using {{Phragmites Australis}} and {{Cyperus Papyrus}}},
  shorttitle = {Treatment of Municipal Wastewater by Vertical Subsurface Flow Constructed Wetland},
  author = {{Garc{\'i}a-{\'A}vila}, Fernando},
  year = {2020},
  month = jun,
  volume = {30},
  pages = {105584},
  issn = {2352-3409},
  doi = {10.1016/j.dib.2020.105584},
  url = {https://www.sciencedirect.com/science/article/pii/S2352340920304789},
  urldate = {2021-05-01},
  abstract = {The data of this document highlights a comparative analysis between the Phragmites Australis and Cyperus Papyrus planted in vertical subsurface flow constructed wetland (VSSFCW) independently implemented at pilot scale for the treatment of domestic wastewater. At the exit of the primary lagoon a pipe was installed to feed a constant flow of 0.6 m3/day to each of the two constructed wetlands. Each unit had a retention time of 1.12 days and a hydraulic load rate of 0.2 m/day. To evaluate the efficiency of the treatment, physical, chemical and biological parameters were monitored for three consecutive months. Water samples in the influent and effluent of each experimental wetland were analyzed. At the end of the monitoring, Cyperus Papyrus had a better removal of total phosphorus (50\%), ammoniacal nitrogen (69.69\%), biochemical oxygen demand (69.87\%), chemical oxygen demand (80.69\%), total coliforms (98.08\%) and fecal coliforms (95.61\%). Meanwhile, Phragmites Australis eliminated total solids better (62.85\%). These data may be useful for comparative purposes on domestic wastewater treatment using Phragmites Australis and Cyperus Papyrus planted in VSSFCW.},
  file = {/home/sasha/Zotero/storage/FRP2NX25/S2352340920304789.html},
  journal = {Data in Brief},
  keywords = {R workbook},
  language = {en}
}

@article{hafnerBiologicalHydrogenProduction2007,
  title = {Biological Hydrogen Production from Nitrogen-Deficient Substrates},
  author = {Hafner, S.D.},
  year = {2007},
  volume = {97},
  pages = {435--437},
  journal = {Biotechnology and bioengineering},
  keywords = {R workbook},
  number = {2}
}

@article{hurlbertPseudoreplicationDesignEcological1984,
  title = {Pseudoreplication and the {{Design}} of {{Ecological Field Experiments}}},
  author = {Hurlbert, Stuart H.},
  year = {1984},
  volume = {54},
  pages = {187--211},
  publisher = {{Ecological Society of America}},
  issn = {0012-9615},
  doi = {10.2307/1942661},
  url = {https://www.jstor.org/stable/1942661},
  urldate = {2021-05-05},
  abstract = {Pseudoreplication is defined as the use of inferential statistics to test for treatment effects with data from experiments where either treatments are not replicated (though samples may be) or replicates are not statistically independent. In ANOVA terminology, it is the testing for treatment effects with an error term inappropriate to the hypothesis being considered. Scrutiny of 176 experimental studies published between 1960 and the present revealed that pseudoreplication occurred in 27\% of them, or 48\% of all such studies that applied inferential statistics. The incidence of pseudoreplication is especially high in studies of marine benthos and small mammals. The critical features of controlled experimentation are reviewed. Nondemonic intrusion is defined as the impingement of chance events on an experiment in progress. As a safeguard against both it and preexisting gradients, interspersion of treatments is argued to be an obligatory feature of good design. Especially in small experiments, adequate interspersion can sometimes be assured only by dispensing with strict randomization procedures. Comprehension of this conflict between interspersion and randomization is aided by distinguishing pre-layout (or conventional) and layout-specific alpha (probability of type I error). Suggestions are offered to statisticians and editors of ecological journals as to how ecologists' understanding of experimental design and statistics might be improved.},
  journal = {Ecological Monographs},
  keywords = {R workbook,Statistical methods},
  number = {2}
}

@article{justesenDevelopmentValidationLowcost2019,
  title = {Development and Validation of a Low-Cost Gas Density Method for Measuring Biochemical Methane Potential ({{BMP}})},
  author = {Justesen, Camilla G. and Astals, Sergi and Mortensen, Jacob R. and Thorsen, Rasmus and Koch, Konrad and Weinrich, S{\"o}ren and Triolo, Jin Mi and Hafner, Sasha D.},
  year = {2019},
  month = dec,
  volume = {11},
  pages = {2431},
  doi = {10.3390/w11122431},
  url = {https://www.mdpi.com/2073-4441/11/12/2431},
  urldate = {2019-11-20},
  abstract = {Accurate determination of biochemical methane potential (BMP) is important for both biogas research and practice. However, access to laboratory equipment limits the capacity of small laboratories or biogas plants to conduct reliable BMP assays, especially in low- and middle-income countries. This paper describes the development and validation of a new gas density-based method for measuring BMP (GD-BMP). In the GD-BMP method, biogas composition is determined from biogas density. Biogas density is based on bottle mass loss and biogas volume, and these can be accurately measured using only a standard laboratory scale, inexpensive syringes, and a simple manometer. Results from four experiments carried out in three different laboratories showed that the GD-BMP method is both accurate (no significant bias compared to gravimetric or volumetric methods with biogas analysis by gas chromatography) and precise (\<3\% relative standard deviation is possible). BMP values from the GD-BMP method were also comparable to those measured for the same substrates with an industry standard automated system (AMPTS II) in two independent laboratories (maximum difference 10\%). Additionally, the GD-BMP method was shown to be accurate even in the presence of leakage by excluding leakage from mass loss measurements. The proposed GD-BMP method represents a significant breakthrough for both biogas research and the industry. With it, accurate BMP measurement is possible with only a minimal investment in supplies and equipment.},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  journal = {Water},
  keywords = {BMP-methods bib,OBA use,R workbook},
  language = {en},
  number = {12}
}

@article{kochRoleInoculumOrigin2017,
  title = {The Role of Inoculum's Origin on the Methane Yield of Different Substrates in Biochemical Methane Potential ({{BMP}}) Tests},
  author = {Koch, Konrad and Lippert, Thomas and Drewes, J{\"o}rg E.},
  year = {2017},
  month = nov,
  volume = {243},
  pages = {457--463},
  issn = {0960-8524},
  doi = {10.1016/j.biortech.2017.06.142},
  abstract = {The impact of the inoculum's origin on the methane yield in Biochemical Methane Potential (BMP) tests was investigated. The three most commonly applied inocula were chosen, originating from (i) a digester of a wastewater treatment plant, (ii) an agricultural biogas plant treating manure and energy crops, and (iii) a biowaste treatment plant. The performance of each inoculum was tested with four different substrates, namely sewage sludge, dried whole crop maize, food waste, and microcrystalline cellulose as a typical reference material. The results revealed that the choice of inoculum had no significant impact on the specific methane yield of the tested substrates except for cellulose. Still, the specific methane production rate was significantly influenced by the choice of the inoculum especially for sewage sludge, but also for food waste and cellulose, whereas it became clear that an inoculum adapted to a substrate is beneficial for a speedy digestion.},
  journal = {Bioresource Technology},
  keywords = {BMP,Inoculum effects,R workbook,software paper 1},
  number = {Supplement C}
}

@book{mccullaghGeneralizedLinearModels1989,
  title = {Generalized {{Linear Models}}, {{Second Edition}}},
  author = {McCullagh, P. and Nelder, John A.},
  year = {1989},
  month = aug,
  edition = {Second},
  publisher = {{Chapman and Hall/CRC}},
  isbn = {0-412-31760-5},
  keywords = {R workbook}
}

@article{montesTemperatureAirVelocity2010,
  title = {Temperature and Air Velocity Effects on Ethanol Emission from Corn Silage with the Characteristics of an Exposed Silo Face},
  author = {Montes, Felipe and Hafner, Sasha D. and Rotz, C. Alan and Mitloehner, Frank M.},
  year = {2010},
  volume = {44},
  pages = {1987--1995},
  issn = {1352-2310},
  doi = {10.1016/j.atmosenv.2010.02.037},
  abstract = {Volatile organic compounds (VOCs) from agricultural sources are believed to be an important contributor to tropospheric ozone in some locations. Recent research suggests that silage is a major source of VOCs emitted from agriculture, but only limited data exist on silage emissions. Ethanol is the most abundant VOC emitted from corn silage; therefore, ethanol was used as a representative compound to characterize the pattern of emission over time and to quantify the effect of air velocity and temperature on emission rate. Ethanol emission was measured from corn silage samples removed intact from a bunker silo. Emission rate was monitored over 12 h for a range in air velocity (0.05, 0.5, and 5 m s-1) and temperature (5, 20, and 35 \textdegree C) using a wind tunnel system. Ethanol flux ranged from 0.47 to 210 g m-2 h-1 and 12 h cumulative emission ranged from 8.5 to 260 g m-2. Ethanol flux was highly dependent on exposure time, declining rapidly over the first hour and then continuing to decline more slowly over the duration of the 12 h trials. The 12 h cumulative emission increased by a factor of three with a 30 \textdegree C increase in temperature and by a factor of nine with a 100-fold increase in air velocity. Effects of air velocity, temperature, and air-filled porosity were generally consistent with a conceptual model of VOC emission from silage. Exposure duration, temperature, and air velocity should be taken into consideration when measuring emission rates of VOCs from silage, so emission rate data obtained from studies that utilize low air flow methods are not likely representative of field conditions.},
  journal = {Atmospheric Environment},
  keywords = {ISC 2018 VOC paper,Model paper citation,R workbook,review paper,Silage VOCs},
  number = {16}
}

@article{parodiBlackSoldierFly2020,
  title = {Black Soldier Fly Larvae Show a Stronger Preference for Manure than for a Mass-Rearing Diet},
  author = {Parodi, Alejandro and Dijk, Kim Van and Loon, Joop J. A. Van and Boer, Imke J. M. De and Schelt, Jeroen Van and Zanten, Hannah H. E. Van},
  year = {2020},
  volume = {144},
  pages = {560--565},
  issn = {1439-0418},
  doi = {10.1111/jen.12768},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jen.12768},
  urldate = {2021-05-14},
  abstract = {The attention for black soldier fly larvae (BSFL) as an alternative ingredient for food and feed products is on the rise. While many studies have reported the efficiency of BSFL to bio-convert a wide range of organic waste streams into larval biomass, so far, it is unknown whether BSFL prefer certain waste streams over others when they have the possibility to choose. Here, we performed a choice-test experiment to explore the preference of BSFL when exposed to pig manure and a mass-rearing diet consisting of plant by-products currently used for industrial BSFL production. We found that after 1 hr of exposure to both feeds, BSFL strongly preferred pig manure over the mass-rearing diet. The preference for manure became stronger as larval age increased. Our results provide the first evidence that BSFL express a distinct diet preference. Understanding the reasons for the strong preference for manure is relevant for a diverse array of practical applications and to inform the discussion on insect welfare.},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/jen.12768},
  copyright = {\textcopyright{} 2020 The Authors. Journal of Applied Entomology published by Blackwell Verlag GmbH},
  file = {/home/sasha/Zotero/storage/3EPQSFYR/jen.html},
  journal = {Journal of Applied Entomology},
  keywords = {R workbook},
  language = {en},
  number = {7}
}

@misc{parodiSupplementaryInformationCode2020,
  title = {Supplementary {{Information}} and {{R}} Code from: {{Black}} Soldier Fly Larvae Show a Stronger Preference for Manure than for a Mass-Rearing Diet},
  shorttitle = {Supplementary {{Information}} and {{R}} Code From},
  author = {Parodi, Alejandro},
  year = {2020},
  month = may,
  publisher = {{4TU.ResearchData}},
  doi = {10.4121/uuid:087b8581-71b6-4bac-914b-1807ebcf90b4},
  url = {/articles/dataset/Supplementary_Information_and_R_code_from_Black_soldier_fly_larvae_show_a_stronger_preference_for_manure_than_for_a_mass-rearing_diet/12675629/1},
  urldate = {2021-05-01},
  abstract = {Black soldier fly larvae (BSFL) was reared on two different feeds: a mass rearing diet made of food by-products and pig manure. Preference tests were performed to determine if at different larval ages, BSFL showed a preference for any of the two diets.},
  file = {/home/sasha/Zotero/storage/LQ572BR9/1.html},
  keywords = {R workbook},
  language = {en}
}

@book{steelPrinciplesProceduresStatistics1997,
  title = {Principles and {{Procedures}} of {{Statistics}}: {{A Biometrical Approach}}},
  shorttitle = {Principles and {{Procedures}} of {{Statistics}}},
  author = {Steel, Robert George Douglas},
  year = {1997},
  publisher = {{McGraw-Hill}},
  abstract = {This book provides a thorough treatment of major statistical methods and techniques for those requiring a strong foundation in applied statistics. The basic ideas of inference from data, the principle of fitting models by least squares, and careful interpretation of results are stressed to provide a firm grounding in both principles and procedures. This edition includes modern topics, computer output and analysis, as well as updated real-world data sets. Moreover, there is an extensive chapter on the principles of experimental design.},
  isbn = {978-0-07-061028-6},
  keywords = {R workbook},
  language = {en}
}

@article{wassersteinMovingWorld052019,
  title = {Moving to a {{World Beyond}} ``p {$<$} 0.05''},
  author = {Wasserstein, Ronald L. and Schirm, Allen L. and Lazar, Nicole A.},
  year = {2019},
  month = mar,
  volume = {73},
  pages = {1--19},
  publisher = {{Taylor \& Francis}},
  issn = {0003-1305},
  doi = {10.1080/00031305.2019.1583913},
  url = {https://doi.org/10.1080/00031305.2019.1583913},
  urldate = {2021-05-05},
  annotation = {\_eprint: https://doi.org/10.1080/00031305.2019.1583913},
  file = {/home/sasha/Zotero/storage/9C4B3MJ2/00031305.2019.html},
  journal = {The American Statistician},
  keywords = {R workbook,Top 10 paper (Sasha)},
  number = {sup1}
}

@book{zarBiostatisticalAnalysis1999,
  title = {Biostatistical {{Analysis}}},
  author = {Zar, Jerrold H},
  year = {1999},
  edition = {4th ed},
  publisher = {{Prentice Hall}},
  address = {{Upper Saddle River, N.J}},
  isbn = {0-13-081542-X},
  keywords = {R workbook,Statistical methods},
  lccn = {QH323.5 .Z37 1999}
}