Literatur

Schlüsselwörter

Statistik, Prognose, Modellierung, R, Datenanalyse, Regression

Aden-Buie, G. (2018). Wide and long data. https://www.garrickadenbuie.com/project/tidyexplain/

Ainali. (2007). Standard deviation diagram micro. https://commons.wikimedia.org/w/index.php?curid=3141713

Anscombe, F. J. (1973). Graphs in statistical analysis. The American Statistician, 27(1), 17–21.

Berger, G. (2019, December 10). The Jobs of Tomorrow: LinkedIn’s 2020 Emerging Jobs Report. https://www.linkedin.com/blog/member/career/the-jobs-of-tomorrow-linkedins-2020-emerging-jobs-report

Bortz, J., & Schuster, C. (2010). Statistik für Human- und Sozialwissenschaftler. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-12770-0

Bowne-Anderson, H. (2018). What Data Scientists Really Do, According to 35 Data Scientists. Harvard Business Review. https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists

Broman, K. W., & Woo, K. H. (2018). Data Organization in Spreadsheets. The American Statistician, 72(1), 2–10. https://doi.org/10.1080/00031305.2017.1375989

Bundesamt, S. (2023-003-272023-003-27). Körpermaße nach Altersgruppen und Geschlecht. Statistisches Bundesamt. https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitszustand-Relevantes-Verhalten/Tabellen/liste-koerpermasse.html

Bundesbank, D. (2023). Household wealth and finances in Germany: Results of the 2021 household wealth survey. Deutsche Bundesbank. https://www.bundesbank.de/resource/blob/908924/3ef9d9a4eaeae8a8779ccec3ac464970/mL/2023-04-vermoegensbefragung-data.pdf

Cetinkaya-Rundel, M., & Hardin, J. (2021). Introduction to Modern Statistics. https://openintro-ims.netlify.app/

Cmglee. (2015). English: Geometric visualisation of the variance of the example distribution (2, 4, 4, 4, 5, 5, 7, 9) on w:Standard deviation. https://commons.wikimedia.org/w/index.php?curid=39472834

Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences, 3rd ed (pp. xxviii, 703). Lawrence Erlbaum Associates Publishers.

Downey, A. (2023). Probably overthinking it: How to use data to answer questions, avoid statistical traps, and make better decisions. The University of Chicago Press.

Earth, H. terrae;. K. links:. L. D. von B. (2021). Deutsch: Fünfjährig gemittelte Abweichung der Lufftemperatur in Deutschland vom langjährigem Mittel 1951 bis 1980. https://commons.wikimedia.org/wiki/File:%C3%84nderung_der_Lufttemperatur_in_Deutschland.gif

Fisher, D., & Meyer, M. (2018). Making data visual: A practical guide to using visualization for insight (First edition). O’Reilly.

Fitzmaurice, G. (2017). Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing. Autodesk Research. https://www.research.autodesk.com/publications/same-stats-different-graphs/

flaticon. (2024). Professor. https://www.flaticon.com/de/kostenlose-icons/professor

Forum, W. E. (2020). The Future of Jobs Report 2020. World Economic Forum. https://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf

Gelman, A., Hill, J., & Vehtari, A. (2021). Regression and other stories. Cambridge University Press.

Goren, A., Vaño-Galván, S., Wambier, C. G., McCoy, J., Gomez-Zubiaur, A., Moreno-Arrones, O. M., Shapiro, J., Sinclair, R. D., Gold, M. H., Kovacevic, M., Mesinkovska, N. A., Goldust, M., & Washenik, K. (2020). A preliminary observation: Male pattern hair loss among hospitalized COVID-19 patients in Spain – A potential clue to the role of androgens in COVID-19 severity. Journal of Cosmetic Dermatology, 19(7), 1545–1547. https://doi.org/10.1111/jocd.13443

Haug, S., Castro, R. P., Kwon, M., Filler, A., Kowatsch, T., & Schaub, M. P. (2015). Smartphone use and smartphone addiction among young people in Switzerland. Journal of Behavioral Addictions, 4(4), 299–307. https://doi.org/10.1556/2006.4.2015.037

Horst, A. (2023). Tidy Data. https://allisonhorst.com/

Horst, A. (2024). Statistics Artwork. https://allisonhorst.com/

Hou, J., Walsh, P. P., & Zhang, J. (2015). The dynamics of Human Development Index. The Social Science Journal, 52(3), 331–347. https://doi.org/10.1016/j.soscij.2014.07.003

Ichihara, Y. G., Okabe, M., Iga, K., Tanaka, Y., Musha, K., & Ito, K. (2008). Color universal design: The selection of four easily distinguishable colors for all color vision types. Color Imaging XIII: Processing, Hardcopy, and Applications, 6807, 206–213. https://doi.org/10.1117/12.765420

imgflip. (2024a). Imageflip Meme. https://imgflip.com

imgflip. (2024b). Yoda Meme. https://imgflip.com

International, T. (2017, January 25). Corruption Perceptions Index 2016. Transparency.org. https://www.transparency.org/en/news/corruption-perceptions-index-2016

Ismay, C., & Kim, A. Y.-S. (2020). Statistical inference via data science: A ModernDive into R and the Tidyverse. CRC Press / Taylor & Francis Group. https://moderndive.com/

Kaplan, D. T. (2009). Statistical modeling: A fresh approach. CreateSpace. https://dtkaplan.github.io/SM2-bookdown/

Kwon, M., Kim, D.-J., Cho, H., & Yang, S. (2013). The smartphone addiction scale: Development and validation of a short version for adolescents. PloS One, 8(12), e83558. https://doi.org/10.1371/journal.pone.0083558

Lalwani, P., Mishra, M. K., Chadha, J. S., & Sethi, P. (2022). Customer churn prediction system: A machine learning approach. Computing, 104(2), 271–294. https://doi.org/10.1007/s00607-021-00908-y

Lovett, M. C., & Greenhouse, J. B. (2000). Applying Cognitive Theory to Statistics Instruction. The American Statistician, 54(3), 196–206. https://doi.org/10.1080/00031305.2000.10474545

Lyon, A. (2014). Why are Normal Distributions Normal? The British Journal for the Philosophy of Science, 65(3), 621–649. https://doi.org/10.1093/bjps/axs046

MacKay, R. J., & Oldford, R. W. (2000). Scientific Method, Statistical Method and the Speed of Light. Statistical Science, 15(3), 254–278. https://doi.org/10.1214/ss/1009212817

Maphry. (2009). Seesaw with mean. https://commons.wikimedia.org/w/index.php?curid=79390659

Marks‐Anglin, A., & Chen, Y. (2020). A historical review of publication bias. Research Synthesis Methods, 11(6), 725–742. https://doi.org/10.1002/jrsm.1452

Matthews, R. (2000). Storks Deliver Babies (p= 0.008). Teaching Statistics, 22(2), 36–38. https://doi.org/10.1111/1467-9639.00013

Menk. (2014, July 29). Linear regression. https://texample.net/tikz/examples/linear-regression/

Messerli, F. H. (2012). Chocolate Consumption, Cognitive Function, and Nobel Laureates. New England Journal of Medicine, 367(16), 1562–1564. https://doi.org/10.1056/NEJMon1211064

Mittag, H.-J., & Schüller, K. (2020). Statistik: Eine Einführung mit interaktiven Elementen. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-61912-4

Mulukom, V. van, Muzzulini, B., Rutjens, B., Lissa, C. J. van, & Farias, M. (2020). Psychological impact of COVID-19 pandemic. https://doi.org/10.17605/OSF.IO/TSJNB

Obels, P., Lakens, D., Coles, N. A., Gottfried, J., & Green, S. A. (2020). Analysis of Open Data and Computational Reproducibility in Registered Reports in Psychology. Advances in Methods and Practices in Psychological Science, 3(2), 229–237. https://doi.org/10.1177/2515245920918872

Oestreich, M., & Romberg, O. (2014). Keine Panik vor Statistik!: Erfolg und Spaß im Horrorfach nichttechnischer Studiengänge. Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-04605-7

Okabe, M., & Ito, K. (2023). Color Universal Design (CUD) / Colorblind Barrier Free. https://jfly.uni-koeln.de/color/

Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect (First edition). Basic Books.

Plesser, H. E. (2018). Reproducibility vs. Replicability: A Brief History of a Confused Terminology. Frontiers in Neuroinformatics, 11, 76. https://doi.org/10.3389/fninf.2017.00076

Poldrack, R. (2022). Statistical Thinking for the 21st Century. https://statsthinking21.github.io/statsthinking21-core-site/index.html

Poldrack, R. A. (2023). Statistical thinking: Analyzing data in an uncertain world. Princeton University Press. https://statsthinking21.github.io/statsthinking21-core-site/

Roser, M., Appel, C., & Ritchie, H. (2013). Human height. Our World in Data. https://ourworldindata.org/human-height

Rothstein, H. R. (2014). Publication Bias. In Wiley StatsRef: Statistics Reference Online. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118445112.stat07071

Sauer, S. (2019). Moderne Datenanalyse mit R: Daten einlesen, aufbereiten, visualisieren und modellieren (1. Auflage 2019). Springer. https://www.springer.com/de/book/9783658215866

Scherer, C., Radchuk, V., Staubach, C., Müller, S., Blaum, N., Thulke, H., & Kramer‐Schadt, S. (2019). Seasonal host life‐history processes fuel disease dynamics at different spatial scales. Journal of Animal Ecology, 88(11), 1812–1824. https://doi.org/10.1111/1365-2656.13070

Schwaiger, E., & Tahir, R. (2022). The impact of nomophobia and smartphone presence on fluid intelligence and attention. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 16(1). https://doi.org/10.5817/CP2022-1-5

Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Psychological Science, 22(11), 1359–1366. https://doi.org/10.1177/0956797611417632

Stigler, S. M. (2016). The seven pillars of statistical wisdom. Harvard University Press.

van Panhuis, W. G., Grefenstette, J., Jung, S. Y., Chok, N. S., Cross, A., Eng, H., Lee, B. Y., Zadorozhny, V., Brown, S., Cummings, D., & Burke, D. S. (2013). Contagious Diseases in the United States from 1888 to the Present. New England Journal of Medicine, 369(22), 2152–2158. https://doi.org/10.1056/NEJMms1215400

Ward, A. F., Duke, K., Gneezy, A., & Bos, M. W. (2017). Brain Drain: The Mere Presence of One’s Own Smartphone Reduces Available Cognitive Capacity. Journal of the Association for Consumer Research, 2(2), 140–154. https://doi.org/10.1086/691462

Wickham, H. (2016). Ggplot2: Elegant graphics for data analysis (Second edition). Springer.

Wickham, H. (2023). Tidy-Data-Sinnbild. https://r4ds.hadley.nz/data-tidy#fig-tidy-structure

Wickham, H., & Grolemund, G. (2018). R für Data Science: Daten importieren, bereinigen, umformen, modellieren und visualisieren (F. Langenau, Trans.; 1. Auflage). O’Reilly. https://r4ds.had.co.nz/index.html

Wilke, C. (2019). Fundamentals of data visualization: A primer on making informative and compelling figures (First edition). O’Reilly Media. https://clauswilke.com/dataviz/

Wilke, C. (2024). Wilkelab/practicalgg. Wilke Lab. https://github.com/wilkelab/practicalgg (Original work published 2019)

Youyou, W., Kosinski, M., & Stillwell, D. (2015). Computer-based personality judgments are more accurate than those made by humans. Proceedings of the National Academy of Sciences, 112(4), 1036–1040. https://doi.org/10.1073/pnas.1418680112