Question 1

You have built several predictive models using training data and now you want to assess the models' performance using validation data. Which measure is NOT appropriate for comparing model performance in terms of model bias for continuous outcomes?

Options :

A :

Root Mean Squared Error (RMSE)

B :

Akaike’s Information Criterion (AIC)

C :

Mean Absolute Error (MAE)

D :

Coefficient of Determination (R-squared)

Answer: B

Question 2

When building an ARCH model, an analyst notices that large errors are followed by large errors and small errors by small errors, but the sign of the errors does not matter. This phenomenon is indicative of what type of volatility clustering?

Options :

A :

Asymmetric volatility clustering

B :

Symmetric volatility clustering

C :

Conditional hetoroskedasticity without clustering

D :

Non-conditional volatility

Answer: B

Question 3

When using SAS Visual Statistics to build a generalized linear model for count data, which model settings should you choose to properly account for overdispersion in the data if the initial model assessment indicates that the variance is greater than the mean?

Options :

A :

Use a normal distribution with an identity link function and adjust the model for overdispersion.

B :

Use a Poisson distribution with a log link function, and apply a scale parameter to adjust for overdispersion.

C :

Use a negative binomial distribution with a log link function.

D :

Use a Poisson distribution with a square root link function and adjust the model for overdispersion.

Answer: C

Question 4

You are required to use PROC NEURAL in SAS to construct a neural network model that minimizes overfitting. Which combination of options would be most effective for this purpose?

Options :

A : train=breiman; valdata=myvalid;

B : train=quikprop; hides=2(3);

C : decay=0.001; maxiter=200; train=conjugate;

D : train=resilient; maxiter=500; ridging=0.01;

Answer: C

Question 5

A data scientist is evaluating the performance of a neural network model for a regression problem. After training, the scientist wants to assess the model's ability to generalize to new data. Which of the following metrics is the most appropriate for evaluating the performance of the neural network on a validation dataset?

Options :

A :

Accuracy

B :

Mean Squared Error (MSE)

C :

Area Under the ROC Curve (AUC)

D :

Precision

Answer: B

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