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Non-parametric tests
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- 1what are non-parametric tests? (Why are they good?)They do not involve population parametrics, such as requirements in ANOVA of distribution and independence.
Data can be measured on any scale - 2what are non-parametric tests? (Why are they good?)They do not involve population parametrics, such as requirements in ANOVA of distribution and independence.
Data can be measured on any scale - 3Mann-Whitney U1. non-parametric alternative to two-sample T-tests
2. uses ranks
3. Two-tailed Hypothesis e.g. H0= two predictors give same response
H1= two predictors give a different response - 4Kruskal-Wallis Test1. Can be used for 3+ groups
2. also uses rankings
avge. relative ratings should be about the same to accept H0.
3. Hypotheses: H0= Distributions of all the populations are the same. H1= Distributions of all populations are not the same.
BONUS= entire distributions compared
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- Karteikarten am Non-parametric tests, erstellt von marielouise.k am 16/05/2013.
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