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

Question : An analyst investigates Region (A, B, or C) as an input variable in a logistic regression model.
The analyst discovers that the probability of purchasing a certain item when Region = A is 1. What problem does this illustrate?

1. Collinearity
2. Influential observations
3. Quasi-complete separation
4. Problems that arise due to missing values

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Refer study material




Question : Refer to the following exhibit:
What is a correct interpretation of this graph?


1. The association between the continuous predictor and the binary response is quadratic.
2. The association between the continuous predictor and the log-odds is quadratic.
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4. The association between the binary predictor and the log-odds is quadratic.

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When a binary outcome variable is modeled using logistic regression, it is assumed that the logit transformation of the outcome variable has a linear relationship with the predictor variables. This makes the interpretation of the regression coefficients somewhat tricky.

Here above association between continuous predictor and the log-odds is quadratic.
Y' = a + b1X1 + b2X12 Quadratic







Question : This question will ask you to provide a missing option.
Given the following SAS program:
What option must be added to the program to obtain a
data set containing Pearson statistics?

1. OUTPUT=estimates
2. OUTP=estimates
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4. OUTCORR=estimates

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PROC CORR (options> ;
Data Sets
DATA= Specifies the input data set
OUTH= Specifies the output data set with Hoeffding's statistics
OUTK= Specifies the output data set with Kendall correlation statistics
OUTP= Specifies the output data set with Pearson correlation statistics
OUTS= Specifies the output data set with Spearman correlation statistics
Statistical Analysis
EXCLNPWGT Excludes observations with nonpositive weight values from the analysis
FISHER Requests correlation statistics using Fisher's transformation
HOEFFDING Requests Hoeffding's measure of dependence,
KENDALL Requests Kendall's tau-b
NOMISS Excludes observations with missing analysis values from the analysis
PEARSON Requests Pearson product-moment correlation
SPEARMAN Requests Spearman rank-order correlation
Pearson Correlation Statistics
ALPHA Computes Cronbach's coefficient alpha
COV Computes covariances
CSSCP Computes corrected sums of squares and crossproducts
FISHER Computes correlation statistics based on Fisher's transformation
SINGULAR= Specifies the singularity criterion
SSCP Computes sums of squares and crossproducts
VARDEF= Specifies the divisor for variance calculations
OUTP=output-data-set
OUT=output-data-set
creates an output data set containing Pearson correlation statistics. This data set also includes means, standard deviations, and the number of observations. The value of the _TYPE_ variable is 'CORR.' When you specify the OUTP= option, the Pearson correlations will also be displayed. If you specify the ALPHA option, the output data set also contains six observations with Cronbach's coefficient alpha.

Related Questions

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Question : Select the correct statement which applies to RMSE?
1. It answers the question, "what is the average magnitude of the forecast errors?"
2. Does not indicate the direction of the errors.
3. RMSE is influenced more strongly by large errors than by small errors.
4. RMSE is influenced more strongly by large errors than by small errors.
5. Its range is from 0 to infinity, with 0 being a perfect score
6. Its range is from 0 to infinity, with infinity being a perfect score


1. 1,2,3,4
2. 2,3,4,5
3. 3,4,5,6
4. 1,2,3,5




Question : Which of the following metrics are useful in measuring the accuracy and quality of a recommender system?

1. Cluster Density
2. Support Vector Count
3. Area Under the ROC Curve (AUC)
4. Sum of Absolute Errors



Question : If you want to understanding your data at a glance, seeing how data is skewed towards one end, which is the best fit graph or chart.

1. ROC
2. Lift
3. Gains
4. Box-and-whisker plot


Question : Consider the boxplot below.
Which of the following statements are true?
I. The distribution is skewed right.
II. The interquartile range is about 8.
III. The median is about 10.

1. I only
2. II only
3. III only
4. I and III



Question : Assume some output variable "y" is a linear combination of some independent input variables "A" plus some independent noise "e".
The way the independent variables are combined is defined by a parameter vector B
y=AB+e
where X is an m x n matrix, B is a vector of n unknowns, and b is a vector of m values.
Assuming that m is not equal to n and the columns of X are linearly independent, which expression correctly solves for B?


1. A
2. B
3. C
4. D




Question : This question will ask you to provide missing code segments.
A logistic regression model was fit on a data set where 40% of the outcomes
were events(TARGET=1) and 60% were non-events (TARGET=0).
The analyst knows that the population where the model
will be deployed has 5% events and 95% non-events.
The analyst also knows that the company's profit margin for correctly
targeted events is nine times higher than the company's loss for incorrectly targeted non-event.
Given the following SAS program:
What X and Y values should be added to the program to correctly score the data?

1. X=40, Y=10
2. X=.05, Y=10
3. X=.05, Y=.40
4. X=.10.Y=05