In the wine data set, eliminate the variables: "x6", "x8" and "x9". Do classification with the remaining predictor variables. Calculate the apparent error rate, leaving 30% (200 times) and leaving one with the following classifiers. Give the result in percent, example: 32.00789 (without the symbol %).

E.R Apparent E.R. Dropping 30% E.R. Dropping one

1) Linear Response Response Response

2) Quadratic Response Response Response

3) LRM Response Response Response

4) MULT Response Response Response

CLASSIFY A NEW SUBJECT, with the following features

x1 x2 x3 x4 x5 x7 x10 x11 6.7 0.4 0.5 2.4 0.0 34 0.8 11.6

Place it into one of the three classes, using the different classifiers places it in the class

1) LDA Response (1,2 or 3)

2) QDA Response (1,2 or 3)

3) LRM Response (1,2 or 3)

4) MULTINOM Response (1,2 or 3)