Descriptive Statistics › References

Michael BrenndoerferUpdated December 25, 20252 min read
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  1. Heckert, N. A., & Filliben, J. J. (2003). NIST/SEMATECH e-Handbook of Statistical Methods: Chapter 1, Exploratory Data Analysis. National Institute of Standards and Technology.
  2. NIST/SEMATECH. (2003a). Measures of location.
  3. Silverman, B. W. (1981). Using kernel density estimates to investigate multimodality. Journal of the Royal Statistical Society: Series B, 43(1), 97–99.
  4. Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall.
  5. NIST/SEMATECH. (2003c). Measures of scale.
  6. Edelmann, D. (2026). The optimal standardization factor for the variance of the normal distribution: A historical perspective and recommendations for practice. Journal of Statistical Theory and Practice, 20(1), 49.
  7. NIST/SEMATECH. (2003b). Approximate intervals that contain most of the population values.
  8. NIST/SEMATECH. (2003d). What are outliers in the data?.
  9. NIST/SEMATECH. (2003e). Measures of skewness and kurtosis.
  10. Westfall, P. H. (2014). Kurtosis as peakedness, 1905–2014. R.I.P.. The American Statistician, 68(3), 191–195.
  11. NIST/SEMATECH. (2003h). Box plot.
  12. NIST/SEMATECH. (2003f). What are control charts?.
  13. NIST/SEMATECH. (2003i). What are variables control charts?.
  14. Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223–236.
  15. Intergovernmental Panel on Climate Change. (2012). Changes in climate extremes and their impacts on the natural physical environment. In Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation.
  16. Anscombe, F. J. (1973). Graphs in statistical analysis. The American Statistician, 27(1), 17–21.
  17. NIST/SEMATECH. (2003g). Confidence limits for the mean.
  18. Simpson, E. H. (1951). The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society: Series B, 13(2), 238–241.
  19. Pearl, J. (2009). Simpson's paradox, confounding, and collapsibility. In Causality: Models, Reasoning, and Inference (2nd ed., pp. 173–200). Cambridge University Press.