SAS Certified BI Content Developer for SAS 9 and Business Analytics Questions and Answer (Dumps and Practice Questions)

Question : Both the MAE and RMSE can range from to infinite, higher values are better.

1. True
2. False



Correct Answer : 1
Mean absolute error (MAE)
The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library references. Expressed in words, the MAE is the average over the verification sample of the absolute values of the differences between forecast and the corresponding observation. The MAE is a linear score which means that all the individual differences are weighted equally in the average.
Root means squared error (RMSE)
The RMSE is a quadratic scoring rule which measures the average magnitude of the error. The equation for the RMSE is given in both of the references. Expressing the formula in words, the difference between forecast and corresponding observed values are each squared and then averaged over the sample. Finally, the square root of the average is taken. Since the errors are squared before they are averaged, the RMSE gives a relatively high weight to large errors. This means the RMSE is most useful when large errors are particularly undesirable.
The MAE and the RMSE can be used together to diagnose the variation in the errors in a set of forecasts. The RMSE will always be larger or equal to the MAE; the greater difference between them, the greater the variance in the individual errors in the sample. If the RMSE=MAE, then all the errors are of the same magnitude
Both the MAE and RMSE can range from 0 to ?. They are negatively-oriented scores: Lower values are better.






Question : A confusion matrix is created for data that were oversampled due to a rare target.
What values are not affected by this oversampling?




1. Sensitivity and PV+
2. Specificity and PV
3. PV+ and PVD.
4. Sensitivity and Specificity


Correct Answer : 4

A confusion matrix involves a comparison of predicted values to actual values.
The classification accuracy rate (Acc), sensitivity (Sen), specificity (Spec) and
precision rate (Pre) were chosen as the criteria in measuring the performance of the Decision Tree model.

Refer study notes as well.




Question : RMSE is most useful when large errors are particularly undesirable.

1. True
2. False



Correct Answer : 1


Explanation: Mean absolute error (MAE)
The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library references. Expressed in words, the MAE is the average over the verification sample of the absolute values of the differences between forecast and the corresponding observation. The MAE is a linear score which means that all the individual differences are weighted equally in the average.
Root means squared error (RMSE)
The RMSE is a quadratic scoring rule which measures the average magnitude of the error. The equation for the RMSE is given in both of the references. Expressing the formula in words, the difference between forecast and corresponding observed values are each squared and then averaged over the sample. Finally, the square root of the average is taken. Since the errors are squared before they are averaged, the RMSE gives a relatively high weight to large errors. This means the RMSE is most useful when large errors are particularly undesirable.
The MAE and the RMSE can be used together to diagnose the variation in the errors in a set of forecasts. The RMSE will always be larger or equal to the MAE; the greater difference between them, the greater the variance in the individual errors in the sample. If the RMSE=MAE, then all the errors are of the same magnitude
Both the MAE and RMSE can range from 0 to ?. They are negatively-oriented scores: Lower values are better.

Related Questions

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Question : Refer to the REG procedure output:
An analyst has selected this model as a champion because it shows better model
fit than a competing model with more predictors.
Which statistic justifies this rationale?

1. R-Square
2. Coeff Var
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4. Error DF


Question : The selection criterion used in the forward selection method in the REG procedure is:

1. Adjusted R-Square
2. SLE
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4. AIC


Question : Which SAS program will correctly use backward elimination selection criterion within the REG procedure?

1. A
2. B
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4. D


Question : Refer to the REG procedure output:
The Intercept estimate is interpreted as:

1. The predicted value of the response when all the predictors are at their current values.
2. The predicted value of the response when all predictors are at their means.
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4. The predicted value of the response when all predictors are at their minimum values.


Question : A linear model has the following characteristics:
A dependent variable (y)
Three continuous predictor variables (x1-x3)
One categorical predictor variable (c1with 3 levels)
Which SAS program fits this model?


1. A
2. B
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4. D


Question : Which SAS program will detect collinearity in a multiple regression application?

1. A
2. B
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4. D