Business Analysis

Business Analysis Statistics Test #3

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

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

Make parameter estimations less stable and make overfitting more likely.

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

Destabilize parameter estimations to reduce overfitting danger.

Correct! Wrong!

The correct answer:
Enhance parameter estimates' stability and cut down on overfitting danger.

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.)

Please select 2 correct answers

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

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

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

By combining similar stores using Greenacre's approach, you can cluster.

Correct! Wrong!

Which interpretation of the estimate is accurate?

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

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

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

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

Correct! Wrong!

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

Regression models with redundant input variables may:

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

Destabilize parameter estimations to reduce overfitting danger.

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

Make parameter estimations less stable and make overfitting more likely.

Correct! Wrong!

The correct answer:
Make parameter estimations less stable and make overfitting more likely.

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

Please select 2 correct answers

Simple random sampling with replacement

Sequential random sampling with replacement

Simple random sampling without replacement

Stratified random sampling without replacement

Correct! Wrong!

The correct answer:
Stratified random sampling without replacement
Simple random sampling without replacement

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

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

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

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

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

Correct! Wrong!

The correct answer:
Training: 50% Validation: 50% Testing: 0%

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?

Only the intercept estimate is biased

Only the non-intercept parameter estimates are biased.

Sensitivity estimates are biased

All parameter estimates are biased.

Correct! Wrong!

The correct answer:
Only the intercept estimate is biased

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

Sensitivity and Specificity

PV+ and PV-

Specificity and PV-

Sensitivity and PV+

Correct! Wrong!

The correct answer:
Sensitivity and Specificity

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?

Score data=valid1 out=roc;

Score data=valid1 outroc=roc;

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

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

Correct! Wrong!

The correct answer:
Score data=valid1 outroc=roc;

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

Use the SCORE statement in the LOGISTIC procedure.

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

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

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

Correct! Wrong!

The correct answer:
Augment the training data set with new observations and rerun the LOGISTIC procedure

Assume that it costs $10 to solicit a non-responder and earns $200 to do so. On an SAS data set called VALID, the logistic regression model produces a probability score called P R. The responder variable Pinch, a 1/0 variable coded as 1 for the responder, can be found in the VALID data set. When a customer's probability score is higher than 0.05, they will be contacted. Which SAS software in the data set calculates the profit for each customer?

Profit=(P_R>0.05)*Purch*200-(P_R>.05)*(1-Purch)*10;

Profit=(P_R.05)*(1-Purch)*10;

If P_R> 0.05; profit=(P_R>0.05)*Purch*200-(P_R>.05)*(1-Purch)*10;

If P_R> 0.05;profit=(P_R>0.05)*Purch*200+(P_R

Correct! Wrong!

The correct answer:
Profit=(P_R>0.05)*Purch*200-(P_R>.05)*(1-Purch)*10;

Business Analysis Statistics Test #3

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

Which interpretation of the estimate is accurate?

Regression models with redundant input variables may:

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

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

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?

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

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?

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

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