Business Analysis Statistics Test #3

Question 1

Regression models that don't include duplicated input variables can:

Accepted Answer

Enhance parameter estimates' stability and cut down on overfitting danger.

Answer

Improve parameter estimates' stability and raise overfitting's danger.

Answer

Make parameter estimations less stable and make overfitting more likely.

Answer

Destabilize parameter estimations to reduce overfitting danger.

Question 2

An analyst is aware that storeId, a categorical predictor, is a key indicator of the objective. To be a workable predictor in the model, store Id has too many levels. In order to treat them as members of the same class level, the analyst wants to consolidate stores. What are the two most efficient solutions to the issue? (Select two.)

Accepted Answer

Combine stores that are related by using subject-matter expertise.

Answer

Eliminate store_id as a predictor in the model because it has too many levels to be feasible.

Answer

To maintain the stochastic variety in the observations, randomly group the stores into five groups.

Question 3

Which interpretation of the estimate is accurate?

Accepted Answer

For every $1,000 increase in wage, the likelihood of the incident increases by 1.142.

Answer

For every $1,000 increase in salary, the likelihood of the incident increases by 1.128.

Answer

For every dollar of pay rise, the likelihood of the incident increases by 1.142.

Answer

For every dollar of pay rise, the likelihood of the incident increases by 1.142.

Question 4

Regression models with redundant input variables may:

Accepted Answer

Make parameter estimations less stable and make overfitting more likely.

Answer

Improve parameter estimates' stability and raise overfitting's danger.

Answer

Destabilize parameter estimations to reduce overfitting danger.

Answer

Enhance parameter estimates' stability and cut down on overfitting danger.

Question 5

Which sampling techniques are suitable for data partitioning for model evaluation?

Accepted Answer

Simple random sampling without replacement

Answer

Simple random sampling with replacement

Answer

Sequential random sampling with replacement

Question 6

What is a reasonable separation between training, validation, and testing data to undertake an honest evaluation of a predictive model?

Accepted Answer

Training: 50% Validation: 50% Testing: 0%

Answer

Training: 100% Validation: 0% Testing: 0%

Answer

Training: 50% Validation: 0% Testing: 50%

Answer

Training: 0% Validation: 100% Testing: 0%

Question 7

Building a model that disproportionately over-represents those cases with an event occuring (e.g., a 50-50 event/non-event split) is a typical strategy for forecasting uncommon events in the LOGISTIprocedure. What issue does this bring up?

Accepted Answer

Only the intercept estimate is biased

Answer

Only the non-intercept parameter estimates are biased.

Answer

Sensitivity estimates are biased

Answer

All parameter estimates are biased.

Question 8

Data that were oversampled as a result of an uncommon target are turned into a confusion matrix. Which variables are unaffected by this oversampling?

Accepted Answer

Sensitivity and Specificity

Answer

PV+ and PV-

Answer

Specificity and PV-

Answer

Sensitivity and PV+

Question 9

A model is created by an analyst utilizing the LOGISTIC process. The sensitivity and specificity statistics on a validation data set for various cutoff values are now of interest to them. What combination of options and statements will provide these statistics?

Accepted Answer

Score data=valid1 outroc=roc;

Answer

Score data=valid1 out=roc;

Answer

Mode1resp(event= '1') = gender region/outroc=roc;

Answer

Mode1resp(event"1") = gender region/ out=roc;

Question 10

Which approach DOES NOT work well for scoring fresh data against a predetermined target in a logistic regression model?

Accepted Answer

Augment the training data set with new observations and rerun the LOGISTIC procedure.

Answer

Use the SCORE statement in the LOGISTIC procedure.

Answer

Augment the training data set with new observations and set their responses to missing.

Answer

Use the saved parameter estimates from the LOGISTIC procedure and score new observations in the SCORE procedure.