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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 of the multivariate observations is then displayed as the collection of these kind of plots and it can be shown that those functions which remain close together for all values of t correspond to the observations which are close to one another in the terms of their Euclidean distance. This property states that such plots can often be used to detect groups of similar observations and identify outliers in multivariate data both. The example given in the Fig drawn below consists of plots for the sample of 30 observations each of which having five variable values.
The plot signifies the presence of three groups in the data. These type of plots can cope only with a reasonable number of observations before becoming very complicated to unravel.
QUESTION ONE. (a) The probability that, a bomber hits a target on a bombing mission is 0.70 Three bombers are sent to bomb a particular target. (i) What is the probability
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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
CALCULATE THE PERCENTAGE OF REFUNDS EXPECTED TO EXCEED $1000 UNDER THE CURRENT WITHHOLDING GUIDELINES
Sequential Sampling Under this method, a number of sample lots are drawn one after another from a universe depending on the results of the earlier samples. Such sampling is gen
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Consider the following game: (a) If (top, left) is a Weakly Dominant Strategy Equilibrium, then what inequalities must hold among (a, ..., h)? (b) If (top, left) is a Na
A real estate agency collected the data shown below, where y = sales price of a house (in thousands of dollars) x 1 = home size (in hundreds of square f
Correspondence Analysis (CA) is a generalization of PCA to contingency tables. The factors of correspondence analysis give an orthogonal decomposi:ion of the Chi- square associated
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