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Normality - Reasons for Screening Data
Prior to analyzing multivariate normality, one should consider univariate normality
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.
Multivariate analysis of variance is the procedure for testing equality of the mean vectors of more than two populations for the multivariate response variable. The method is dire
Can I use ICC for this kind of data? Wind Month Day Temp(DV) 7.4 5 1 67 8 5 2 72 12.6 5 3 74 11.5 5 4 62 I am taking temp as the dependent variable. There are many more values.
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
An oil company is considering whether or not to bid for an offshore drilling contract. If they bid, the value would be $600m with a 65% chance of gaining the contract. The company
Nearest-neighbour methods are the methods of discriminant analysis are based on studying the training set subjects much similar to the subject to be classified. Classification mig
Bayesian confidence interval : An interval of the posterior distribution which is so that the density of it at any point inside the interval is greater than that of the density at
Individual differences scaling is a form of multidimensional scaling applicable to the data comprising of a number of proximity matrices from the different sources that is differe
The method or technique for displaying the relationships between categorical variables in a type of the scatter plot diagram. For two this type of variables displayed in the form o
explain the graphical method of measure of central tendency
Misspecification is the term is applied to describe the assumed statistical models which are incorrect for one of the several of reasons, for instance, using the wrong probability
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