XLSTAT - Normality tests

View a tutorial

Assuming a sample is normally distributed is common in statistics. But checking that this is actually true is often neglected. For example, the normality of residuals obtained in linear regression is rarely tested, even though it governs the quality of the confidence intervals surrounding parameters and predictions.

Normality tests in XLSTAT

XLSTAT offers four tests for testing the normality of a sample:

• The Shapiro-Wilk test
This test is best suited to samples of less than 5000 observations;
• The Anderson-Darling test
This test proposed by Stephens (1974) is a modification of the Kolmogorov-Smirnov test and is suited to several distributions including the normal distribution for cases where the parameters of the distribution are not known and have to be estimated;
• The Lilliefors test
This test is a modification of the Kolmogorov-Smirnov test and is suited to normal cases where the parameters of the distribution, the mean and the variance are not known and have to be estimated;
• The Jarque-Bera test
This test is more powerful the higher the number of values.

Plots associated to the Normality test

In order to check visually if a sample follows a normal distribution, it is possible to:

• P-P Plots (normal distribution)
P-P plots (for Probability-Probability) are used to compare the empirical distribution function of a sample with that of a sample distributed according to a normal distribution of the same mean and variance. If the sample follows a normal distribution, the points will lie along the first bisector of the plan.
• Q-Q Plots (normal distribution)
Q-Q plots (for Quantile-Quantile) are used to compare the quantities of the sample with those of a sample distributed according to a normal distribution of the same mean and variance. If the sample follows a normal distribution, the points will lie along the first bisector of the plan.

This tool complements the "Distribution fitting" tool, which allows you to determine the value of the parameters of the normal distribution and to test the goodness of fit using a Chi-square or a Kolmogorov Smirnov test.

This analysis is available in the XLStat-Base addin for Microsoft Excel

Kovach Computing Services (KCS) was founded in 1993 by Dr. Warren Kovach. The company specializes in the development and marketing of inexpensive and easy-to-use statistical software for scientists, as well as in data analysis consulting.

Get in Touch

• Email:
sales@kovcomp.com