Normality - reasons for screening data, Advanced Statistics

Normality - Reasons for Screening Data

Prior to analyzing multivariate normality, one should consider univariate normality

  • Histogram, Normal Q-Qplot (values on x axis with expected normal values on the y axis)
  • Skewness and Kurtosis (null hypothesis: values around zero with alpha levels of .01 or .001
  • Kolmogorov-Smirnov Test

 

Multivariate normality refers to a normal distribution of combination of variables (two-by-two, plus all linear combination of the variables) Univariate normality is a necessary but not sufficient condition for multivariate normality.

For bivariate normality one should check all the two-by-two scatter plots (they should have elliptical shape)

Sometimes data transformation is necessary for normality.

 

Posted Date: 3/4/2013 6:25:28 AM | Location : United States







Related Discussions:- Normality - reasons for screening data, Assignment Help, Ask Question on Normality - reasons for screening data, Get Answer, Expert's Help, Normality - reasons for screening data Discussions

Write discussion on Normality - reasons for screening data
Your posts are moderated
Related Questions
Non linear mapping (NLM ) is a technique for obtaining a low-dimensional representation of the set of multivariate data, which operates by minimizing a function of the differences


Interval-censored observations are the  observations which often occur in the context of studies of time elapsed to the particular event when subjects are not monitored regularl

Designs in which the information on main effects and low-order inter- actions are attained by running only the fraction of the complete factorial experiment and supposing that part

Cascadedparameters: A group of parameters which is interlinked and where selecting the value for the ?rst parameter affects the choice and option available in the subsequent param


O'Brien's two-sample tests are the extensions of the conventional tests for assessing the differences between treatment groups which take account of the possible heterogeneous nat

Geo statistics: The body of methods useful for understanding and modelling spatial variability in a course of interest. Central to these techniques is the idea that measurements t

Kaiser's rule is the  rule frequently used in the principal components analysis for selecting the suitable the number of components. When the components are derived from correlati

Confounding:  A procedure observed in some factorial designs in which it is impossible to differentiate between some main effects or interactions, on the basis of the particular d