Question 1(10 Points): Ia the following True/Falso questions. ploase say True or

Question

Question 1(10 Points): Ia the following True/Falso questions. ploase say True or False for ench onc ONLY (a) MANOVA test is rolbust to the ontliensC (b) Larger values of the Willks Lamtxia (A) test indicate larger vorinbility betwoen the voctor of meus() (e) In Discriminant onalysis, the min goal is to classify new observations (d) Principal component analysis is an appropriate teclnique to ncasure the corrclation between two sets of variables (e) Varimax methol is a non-orthogonal rotatiou mcthod that minimizes the umiber of variables that have high londings on ench factor (c) Redundancy is neasured by the product of the R-Mlture of divided by p given x (f) If the random vector X has mean ux and covariance matrix Ex, and tho random voctor Y has mcan ,y, and covariance matrix ).. then X-y will have mean #x-PV and covariance matrix Er + Er (8) In the variance-covariance matrix. tho product of cigenvalucs oquals to the generalized variance (h) Statistical distauce doos not account for difference in variation & the present of correlation (i) The Likelihood Ration Test is the division of the unrestricted by the re- stricted maximum likelihood cstimators

Explanation / Answer

(a) False: MANOVA is notrobust to outliers. It is in fact sensitive to outliers

(b) True : the closer to zero Wilks Lambda is, the more the variable in question contributes to the model. You would reject the null hypothesis when Wilk’s lambda is close to zero, although this should be done in combination with a small p-value.

(c) False : Discriminant function analysis is useful in determining whether a set of variables is effective in predicting category membership

(d) False: Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components

(e) False : Varimax is so called because it maximizes the sum of the variances of the squared loadings

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