Abstract
As outlined in Chapter 1 the techniques of statistical inference usually require that assumptions be made regarding the sample data. Such assumptions usually include the type of sampling process that produced the data and in some cases the nature of the population distribution from which the sample was drawn. When assumptions are violated the techniques employed can lead to misleading results. Good statistical practice therefore requires that the data be studied in detail before statistical inference procedures are applied. The techniques of exploratory data analysis are designed to provide such a preliminary view.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Barnett, V. and Lewis, T. (1978). Outliers in Statistical Data, New York: John Wiley and Sons Inc.
Dixon, W.J. (1950). “Analysis of Extreme Values,” Annals of Mathematical Statistics 21, 488–506.
Dixon, W.J. (1951). “Ratios Involving Extreme Values,” Annals of Mathematical Statistics 22, 68–78.
Dixon, W.J. and Kronmal (1965). “The Choice of Origin and Scale for Graphs,” Journal of the Association for Computing Machinery 12, 259–261.
Filliben, J.J. (1975). “The Probability Plot Correlation Coefficient Test for Normality,” Technometrics 17, 111–117.
Grubbs, F.E. and Beck, G. (1972). “Extension of Sample Sizes and Percentage Points for Significance Tests of Outlying Observations,” Technometrics 14, 847–854.
Hinkley, D.V. (1975). “On Power Transformations to Symmetry,” Biometrika 62, 101–111.
Hoaglin, C., Mosteller, F. and Tukey, J.W. (1985). Exploring Data, Tables, Trends and Shapes. New York: John Wiley and Sons Inc.
Hoaglin, David C., Iglewicz, Boris and Tukey, John W. (1986). “Performance of Some Resistant Rules for Outlier Labelling,” Journal of the American Statistical Association 81, 991–999.
Hogg, Robert V. (1974). “Adaptive Robust Procedures: A Partial Review and Some Suggestions for Future Applications and Theory,” Journal of the American Statistical Association 69, 909–923.
John, J.A. and Draper, N.R. (1980). “An Alternative Family of Transformations,” Applied Statistics 29, 190–197.
Johnson, R.A. and Wichern, D.W. (1982). Applied Multivariate Statistical Analysis. Englewood Cliffs, N.J.: Prentice-Hall.
Lilliefors, H.W. (1967). “On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown,” Journal of the American Statistical Association 62, 399–402.
Lin, C. and Mudhulkar, G.S. (1980). “A Simple Test for Normality Against Asymmetric Alternatives,” Biometrika 67, 455–461.
Mardia, K.V. (1980). “Tests of Univariate and Multivariate Normality” in P.R. Krishnaiah ed., Handbook of Statistics, Vol. 1. New York: North Holland.
Miller, Rupert G. (1986). Beyond Anova, Basics of Applied Statistics. New York: John Wiley and Sons Inc.
Moors, J.J.A. (1988). “A Quantile Alternative for Kurtosis,” Statistician 37, 25–32.
Rosenberger, James L. and Gasko, Miriam (1983). “Comparing Location Estimators: Trimmed Means, Medians and Trimean” in Hoaglin,Mosteller and Tukey ed., Understanding Robust and Exploratory Data Analysis. New York: John Wiley.
Shiftier, Ron E. (1987). “Bounds for the Maximum Z-Score,” Teaching Statistics 9, 80–81.
Stephans, M.A. (1974). “EDF Statistics for Goodness of Fit and Some Comparisons,” Journal of the American Statistical Association 69, 730–737.
Sturges, H.A. (1926). “The Choice of a Class Interval,” Journal of the American Statistical Association 21, 65–66.
Velleman, P.F. (1976). “Interactive Computing for Exploratory Data Analysis I: Display Algorithms,” 1975 Proceedings of the Statistical Computing Section. Washington, D.C.: American Statistical Association.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1991 Springer Science+Business Media New York
About this chapter
Cite this chapter
Jobson, J.D. (1991). Univariate Data Analysis. In: Applied Multivariate Data Analysis. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0955-3_2
Download citation
DOI: https://doi.org/10.1007/978-1-4612-0955-3_2
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-6960-1
Online ISBN: 978-1-4612-0955-3
eBook Packages: Springer Book Archive