Types of Data › References
Michael BrenndoerferUpdated December 25, 20252 min read
Machine Learning from ScratchAll chapters Part 1 of 9Statistics 101
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- Altman, D. G., & Royston, P. (2006). The cost of dichotomising continuous variables. BMJ, 332(7549), 1080.
- Australian Bureau of Statistics. (2023a). Variables.
- Australian Bureau of Statistics. (2023b). Quantitative and qualitative data.
- Joint Committee for Guides in Metrology. (2012). International Vocabulary of Metrology—Basic and General Concepts and Associated Terms (VIM), 3rd edition.
- Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14. DOI.
- Lord, F. M. (1953). On the statistical treatment of football numbers. American Psychologist, 8(12), 750–751.
- National Institute of Standards and Technology. (n.d.-a). What is a probability distribution?.
- National Institute of Standards and Technology. (n.d.-b). Detection of outliers.
- National Institute of Standards and Technology. (2010). Unit conversion.
- Pennsylvania State University. (n.d.-a). Choosing appropriate statistical methods.
- Pennsylvania State University. (n.d.-b). Correlation.
- Pennsylvania State University. (n.d.-c). Poisson regression.
- Polyanskiy, Y. (2018). Continuous random variables. MIT OpenCourseWare.
- scikit-learn developers. (n.d.-a). Importance of feature scaling.
- scikit-learn developers. (n.d.-b). Encoding categorical features.
- Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103(2684), 677–680.
- UK Data Service. (2021). Data documentation: quantitative data.
- Velleman, P. F., & Wilkinson, L. (1993). Nominal, ordinal, interval, and ratio typologies are misleading. The American Statistician, 47(1), 65–72.