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The model for data containing continuous and categorical variables both.The categorical data are summarized by the contingency table and their marginal distribution, 182by the multinomial distribution. The continuous variables are supposed to have a multivariate normal distribution in which the means of the variables are permitted to vary from cell to cell of the contingency table, but with the variance-covariance matrix of variables being common to all cells. When there is the single categorical variable with two categories the model becomes that supposed by Fisher's linear discriminant analysis.
Kleiner Hartigan trees is a technique for displaying the multivariate data graphically as the 'trees' in which the values of the variables are coded into length of the terminal br
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
Bubble plot : A method or technique for displaying the observations which involve three variable values. Two of the variables are used to make a scatter diagram and values of the t
VIF is the abbreviation of variance inflation factor which is a measure of the amount of multicollinearity that exists in a set of multiple regression variables. *The VIF value
Matching is the method of making a study group and a comparison group comparable with respect to the extraneous factors. Generally used in the retrospective studies when selecting
The Null Hypothesis - H0: γ 1 = γ 2 = ... = 0 i.e. there is no heteroscedasticity in the model The Alternative Hypothesis - H1: at least one of the γ i 's are not equal
The growth in bad debt expense for Johnston office supply Company over this time period.If this rate continues,estimate the percentage increase in bad debts for 1997,relative to 19
Laplace distribution : The probability distribution, f(x), given by the following formula Can be derived as the distribution of the difference of two independent random var
An analyst counted 17 A/B runs and 26 time series observations. Do these results suggest that the data are nonrandom? Explain
Principal components analysis is a process for analysing multivariate data which transforms original variables into the new ones which are uncorrelated and account for decreasing
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