March 2014
Intermediate to advanced
306 pages
5h 47m
English
Content preview from Statistical Hypothesis Testing with SAS and R
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Chapter 11
Tests on normality
In this chapter we present goodness-of-fit tests for the Gaussian distribution. In Section 11.1 tests based on the empirical distribution function (EDF) are treated. A good resource for this kind of test is Stephens (1986). We start with the Kolmogorov–Smirnov test. It evaluates the greatest vertical distance between the EDF and the theoretical cumulative distribution function (CDF). If both, or one parameter are estimated from the sample the distribution of the test statistic changes and the test is called the Lilliefors test on normality.
Section 11.1.1 deals with tests not based on the EDF such as the Jarque–Bera test which compares observed and expected moments of the normal distribution.
11.1 Tests based on the EDF
11.1.1 Kolmogorov–Smirnov test (Lilliefors test for normality)
| Description: | Tests if a sample is sampled from a normal distribution with parameter  and  . |
| Assumptions: |
|
are identically, independently distributed with observations 
and a continuous distribution ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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