QUESTION 1. Which of the following data mining methods in XLMINER is especially

Question

QUESTION 1. Which of the following data mining methods in XLMINER is especially suited for (and limited to) both categorical predictor and outcome variable?

a. Naïve Bayes method.

b. Regression

c. Neural Network

d. K-Nearest Neighbor method.

QUESTION 2. Which of the following statement(s) is(are) correct?

a. In multiple linear regression, dropping predictors that are uncorrelated with the dependent variable may decrease the variance of predictions.

b. In multiple linear regression, using predictors that are actually uncorrelated with the dependent variable may decrease the variance of predictions.

c. Both a. and b.

d. Neither a. nor b.

Explanation / Answer

QUESTION 1. Which of the following data mining methods in XLMINER is especially suited for (and limited to) both categorical predictor and outcome variable?

a. Naïve Bayes method.

b. Regression

c. Neural Network

d. K-Nearest Neighbor method.

Answer)

The Naïve Bayes method of the data mining methods in XLMINER is especially suited for both categorical predictor and outcome variable. Thus the answer is:

a. Naïve Bayes method.

QUESTION 2. Which of the following statement(s) is(are) correct?

a. In multiple linear regression, dropping predictors that are uncorrelated with the dependent variable may decrease the variance of predictions.

b. In multiple linear regression, using predictors that are actually uncorrelated with the dependent variable may decrease the variance of predictions.

c. Both a. and b.

d. Neither a. nor b.

Answer)

In multiple linear regression, using predictors that are actually uncorrelated with the dependent variable may increase the variance of predictions.

In multiple linear regression, dropping predictors that are uncorrelated with the dependent variable may increase the average error bias of predictions.

Thus the correct answer as both the statements given are False would be:

d. Neither a. nor b.

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