Use of the Correlation Coefficient with Normal Probability Plots

Abstract The use of the correlation coefficient is suggested as a technique for summarizing and objectively evaluating the information contained in probability plots. Goodness-of-fit tests are constructed using this technique for several commonly used plotting positions for the normal distribution. Empirical sampling methods are used to construct the null distribution for these tests, which are then compared on the basis of power against certain nonnormal alternatives. Commonly used regression tests of fit are also included in the comparisons. The results indicate that use of the plotting position pi = (i - .375)/(n + .25) yields a competitive regression test of fit for normality.

[1]  Allen Hazen,et al.  Closure of "Storage to be Provided in Impounding Municipal Water Supply" , 1914 .

[2]  W. Weibull,et al.  The phenomenon of rupture in solids , 1939 .

[3]  A. Benard,et al.  Het uitzetten van waarnemingen op waarschijnlijkheids‐papier1 , 1953 .

[4]  A. Bernard,et al.  The plotting of observations on probability-paper , 1955 .

[5]  F. David,et al.  Statistical Estimates and Transformed Beta-Variables. , 1960 .

[6]  Bradford F. Kimball,et al.  On the Choice of Plotting Positions on Probability Paper , 1960 .

[7]  J. Tukey The Future of Data Analysis , 1962 .

[8]  G. W. Snedecor Statistical Methods , 1964 .

[9]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[10]  Irene A. Stegun,et al.  Handbook of Mathematical Functions. , 1966 .

[11]  H. Lilliefors On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown , 1967 .

[12]  S. Shapiro,et al.  An Approximate Analysis of Variance Test for Normality , 1972 .

[13]  K. S. Kölbig,et al.  Errata: Milton Abramowitz and Irene A. Stegun, editors, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, National Bureau of Standards, Applied Mathematics Series, No. 55, U.S. Government Printing Office, Washington, D.C., 1994, and all known reprints , 1972 .

[14]  Richard P. Brent,et al.  Algorithm 488: A Gaussian pseudo-random number generator , 1974, Commun. ACM.

[15]  J. Filliben The Probability Plot Correlation Coefficient Test for Normality , 1975 .

[16]  V. Barnett Probability Plotting Methods and Order Statistics , 1975 .

[17]  John R LaBrecque Goodness-of-Fit Tests Based on Nonlinearity in Probability Plots , 1977 .

[18]  E. S. Pearson,et al.  Tests for departure from normality: Comparison of powers , 1977 .

[19]  C. Cunnane Unbiased plotting positions — A review , 1978 .

[20]  Samuel S. Shapiro,et al.  A Review of Distributional Testing Procedures and Development of a Censored Sample Distributional Test , 1981 .

[21]  James R. King,et al.  Probability Charts for Decision Making , 1981 .

[22]  Wayne Nelson,et al.  Applied life data analysis , 1983 .

[23]  D. Mage An Objective Graphical Method for Testing Normal Distributional Assumptions using Probability Plots , 1982 .

[24]  W. D. Stirling,et al.  Enhancements to Aid Interpretation of Probability Plots , 1982 .

[25]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[26]  W. J. Dixon,et al.  BMDP statistical software : 1981 , 1982 .

[27]  I. D. Hill,et al.  An Efficient and Portable Pseudo‐Random Number Generator , 1982 .

[28]  I. D. Hill,et al.  Correction: Algorithm AS 183: An Efficient and Portable Pseudo-Random Number Generator , 1982 .

[29]  J. Royston Expected Normal Order Statistics (Exact and Approximate) , 1982 .

[30]  J. Royston The W Test for Normality , 1982 .

[31]  J. P. Royston,et al.  Algorithm AS 181: The W Test for Normality , 1982 .

[32]  J. R. Michael The stabilized probability plot , 1983 .

[33]  インターグループ SAS user's guide : basics , 1986 .