For a given experiment, How to verify that the observations are independent? How to verify the errors are normally distributed?
Independence is determined based on knowledge of the experiment, ie measurements on siblings are not independent or multiple measurements om the same individual.
Normal an distribution can be verified by looking at a histogram - proc univariate - and normality tests also available via proc univariate.
Independence is determined based on knowledge of the experiment, ie measurements on siblings are not independent or multiple measurements om the same individual.
Normal an distribution can be verified by looking at a histogram - proc univariate - and normality tests also available via proc univariate.
What other methods are availble to test the normality?
Shapiro-Wilk test, two dead Russians test (Kolmogorov-Smirnov), QQ-plot.
The tests all suffer from the same kind of thing--if you have enough data to actually do the test, even miniscule differences from normality seem to trigger rejection of the null hypothesis.
Thus, I think the consensus these days is to look at the QQ plot, and see if there are noticeable shifts away from the diagonal.
See Rick Wicklin's blog. Here is a good start:
http://blogs.sas.com/content/iml/2011/10/28/modeling-the-distribution-of-data-create-a-qq-plot.html
Steve Denham
Don’t miss the livestream kicking off May 7. It’s free. It’s easy. And it’s the best seat in the house.
Join us virtually with our complimentary SAS Innovate Digital Pass. Watch live or on-demand in multiple languages, with translations available to help you get the most out of every session.
ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.
Find more tutorials on the SAS Users YouTube channel.