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Discriminant analysis (DA) helps to determine which variables discriminate between two or more naturally occurring groups. Mathematically equivalent to MANOVA, it ' is extensively used when a set of explanatory variables are used to predict the group to which a given unit belongs (which is a nominal dependent variables). It cornhines the explanatory variables in order to create the largest F when the groups are used as a fixed factor in an ANOVA.
The model is constructed with a set of observations for which the classes are known. The set of observations are sometimes referred to as the training set.
Histogram: It is generally used for charting continuous frequency distribution. In histogram, data are plotted as a series of rectangle one over the other. Class intervals
how is a free hand graph secular trend method plotted
Assumptions in ANOVA The various populations from which the samples are drawn should be normal and have the same variance. The requirement of normality can be discarded if t
Coefficient of Variation The standard deviation discussed above is an absolute measure of dispersion. The corresponding relative measure is known as the coefficient of vari
Show that the ISB in a bin containing the origin of the double exponen-tial density, f(x) = exp(-|x|)/2, is O(h 3 ); hence, the discontinuity in the derivative of f does not have a
The cornlnunalities h j represent the fraction of the total variance' 'accounted for of variabie j. Ry calculating the communalities we can keep track of how much of-the orig
Andrews ‘Plots A graphical display of multivariate data in which an observation, x0 = [x1, x2, . . . , xq] is represented can be represented in the form of function A set
b. A paper mill produces two grades of paper viz., X and Y. Because of raw material restrictions, it cannot produce more than 400 tons of grade X paper and 300 tons of grade Y
what the purpose we use it
what are the importance, uses,optimums and applications of the following in agriculture field experiments; 1.standard deviation 2.standard error 3. coefficient of variation
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