Exploratory Data Analysis
1.3. EDA Techniques
1.3.5. Quantitative Techniques
Test for Distributional Adequacy
|The Anderson-Darling test (Stephens, 1974) is used to test if a sample of data came from a population with a specific distribution. It is a modification of the Kolmogorov-Smirnov (K-S) test and gives more weight to the tails than does the K-S test. The K-S test is distribution free in the sense that the critical values do not depend on the specific distribution being tested (note that this is true only for a fully specified distribution, i.e. the parameters are known). The Anderson-Darling test makes use of the specific distribution in calculating critical values. This has the advantage of allowing a more sensitive test and the disadvantage that critical values must be calculated for each distribution. Currently, tables of critical values are available for the normal, uniform, lognormal, exponential, Weibull, extreme value type I, generalized Pareto, and logistic distributions. We do not provide the tables of critical values in this Handbook (see Stephens 1974, 1976, 1977, and 1979) since this test is usually applied with a statistical software program that will print the relevant critical values.|
The Anderson-Darling test is defined as:
We generated 1,000 random numbers for normal,
double exponential, Cauchy, and lognormal distributions.
In all four cases, the Anderson-Darling
test was applied to test for a normal distribution.
The normal random numbers were stored in the variable Y1, the double exponential random numbers were stored in the variable Y2, the Cauchy random numbers were stored in the variable Y3, and the lognormal random numbers were stored in the variable Y4.
Distribution Mean Standard Deviation ------------ -------- ------------------ Normal (Y1) 0.004360 1.001816 Double Exponential (Y2) 0.020349 1.321627 Cauchy (Y3) 1.503854 35.130590 Lognormal (Y4) 1.518372 1.719969 H0: the data are normally distributed Ha: the data are not normally distributed Y1 adjusted test statistic: A2 = 0.2576 Y2 adjusted test statistic: A2 = 5.8492 Y3 adjusted test statistic: A2 = 288.7863 Y4 adjusted test statistic: A2 = 83.3935 Significance level: α = 0.05 Critical value: 0.752 Critical region: Reject H0 if A2 > 0.752When the data were generated using a normal distribution, the test statistic was small and the hypothesis of normality was not rejected. When the data were generated using the double exponential, Cauchy, and lognormal distributions, the test statistics were large, and the hypothesis of an underlying normal distribution was rejected at the 0.05 significance level.
The Anderson-Darling test can be used to answer the following
Many statistical tests and procedures are based on specific
distributional assumptions. The assumption of normality
is particularly common in classical statistical tests.
Much reliability modeling is based on the assumption
that the data follow a Weibull distribution.
There are many non-parametric and robust techniques that do not make strong distributional assumptions. However, techniques based on specific distributional assumptions are in general more powerful than non-parametric and robust techniques. Therefore, if the distributional assumptions can be validated, they are generally preferred.
Chi-Square goodness-of-fit Test
Shapiro-Wilk Normality Test
Probability Plot Correlation Coefficient Plot
|Case Study||Josephson junction cryothermometry case study.|
|Software||The Anderson-Darling goodness-of-fit test is available in some general purpose statistical software programs. Both Dataplot code and R code can be used to generate the analyses in this section.|