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NEW QUESTION 18
Refer to the exhibit:
SAS output from the RSQUARE selection method, within the REG procedure, is shown. The top two models in each subset are given.
Based on the AIC statistic, which model is the champion model?
- A. RestPulse
- B. RunTime
- C. Age Weight RunTime RunPulse MaxPulse
- D. Age Weight RunTime RunPulse RestPulse MaxPulse
Answer: C
NEW QUESTION 19
Refer to the exhibit:
Based upon the comparative ROC plot for two competing models, which is the champion model and why?
- A. Candidate 1, because the area outside the curve is greater
- B. Candidate 1, because it is closer to the diagonal reference curve
- C. Candidate 2, because the area under the curve is greater
- D. Candidate 2, because it shows less over fit than Candidate 1
Answer: C
NEW QUESTION 20
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?
- A. OUTCORR=estimates
- B. OUTSTAT=estimates
- C. OUTP=estimates
- D. OUTPUT=estimates
Answer: C
NEW QUESTION 21
Spearman statistics in the CORR procedure are useful for screening for irrelevant variables by
investigating the association between which function of the input variables?
- A. Concordant and discordant pairs of ranked observations
- B. Logit link (log (p/1-p))
- C. Weighted sum of chi-square statistics for 2x2 tables
- D. Rank-ordered values of the variables
Answer: D
NEW QUESTION 22
While building a predictive model, median imputations are performed while preparing the training data.
How should the imputations be addressed in the validation data?
- A. The imputed values must be recalculated using both the training and the validation data.
- B. The imputed values must be applied directly to the validation data without recalculation.
- C. The imputed values are irrelevant to the validation data, and are not used.
- D. The imputed values must be recalculated using the validation data.
Answer: D
NEW QUESTION 23
Refer to the exhibit:
Based upon the comparative ROC plot for two competing models, which is the champion model and why?
- A. Candidate 1, because the area outside the curve is greater
- B. Candidate 1, because it is closer to the diagonal reference curve
- C. Candidate 2, because the area under the curve is greater
- D. Candidate 2, because it shows less over fit than Candidate 1
Answer: C
NEW QUESTION 24
An analyst compares the mean salaries of men and women working at a company.
The SAS data set SALARY contains variables:
Gender (M or F)
Pay (dollars per year)
Which SAS programs can be used to find the p-value for comparing men's salaries with women's salaries?
(Choose two.)
- A. Option D
- B. Option C
- C. Option A
- D. Option B
Answer: C,D
NEW QUESTION 25
Refer to the exhibit.
Based on the control plot, which conclusion is justified regarding the means of the response?
- A. Only XL and 2XL are not significantly different from each other.
- B. 2XL is significantly different from all other groups.
- C. No groups are significantly different from each other.
- D. All groups are significantly different from each other.
Answer: A
NEW QUESTION 26
Which SAS program will divide the original data set into 60% training and 40% validation data sets,
stratified by county?
- A. Option D
- B. Option A
- C. Option C
- D. Option B
Answer: C
NEW QUESTION 27
Refer to the REG procedure output:
How many observations are used in the analysis? Enter your numeric answer in the space below.
Answer:
Explanation:
100
NEW QUESTION 28
What is a drawback to performing data cleansing (imputation, transformations, etc.) on raw data prior to partitioning the data for honest assessment as opposed to performing the data cleansing after partitioning the data?
- A. It violates assumptions of the model.
- B. It omits the training (and test) data sets from the benefits of the cleansing methods.
- C. There is no ability to compare the effectiveness of different cleansing methods.
- D. It requires extra computational effort and time.
Answer: C
NEW QUESTION 29
Refer to the lift chart:
What does the reference line at lift = 1 corresponds to?
- A. The predicted lift if the entire population is scored as event cases
- B. The predicted lift if 50% of the population are randomly scored as event cases
- C. The predicted lift for the best 50% of validation data cases
- D. The predicted lift if none of the population are scored as event cases
Answer: A
NEW QUESTION 30
The standard form of a linear regression model is:
Which statement best summarizes the assumptions placed on the errors?
- A. The errors are correlated, normally distributed with constant mean and zero variance.
- B. The errors are independent, normally distributed with constant mean and zero variance.
- C. The errors are correlated, normally distributed with zero mean and constant variance.
- D. The errors are independent, normally distributed with zero mean and constant variance.
Answer: D
NEW QUESTION 31
The following LOGISTIC procedure output analyzes the relationship between a binary response and an
ordinal predictor variable, wrist_size Using reference cell coding, the analyst selects Large (L) as the
reference level.
What is the estimated logit for a person with large wrist size?
Click the calculator button to display a calculator if needed.
- A. 0.5663
- B. -1.0415
- C. 0.0819
- D. -3.7727
Answer: B
NEW QUESTION 32
Refer to the exhibit.
Given alpha=0.02, which conclusion is justified regarding percentage of body fat, comparing small (S), medium (M), and large (L) wrist sizes?
- A. Large wrist size is significantly different than medium wrist size.
- B. Large wrist size is significantly different than small wrist size.
- C. There is no significant difference due to wrist size.
- D. Medium wrist size is significantly different than small wrist size.
Answer: B
NEW QUESTION 33
Refer to the exhibit.
Output from a multiple linear regression analysis is shown.
What is the most appropriate statement concerning collinearity between the input variables?
- A. Collinearity is not a problem since all Pr>|t| values are less than 0.05.
- B. Collinearity is a problem since all Pr>|t| values are less than 0.05.
- C. Collinearity is a problem since all variance inflation values are less than 10.
- D. Collinearity is not a problem since all variance inflation values are less than 10.
Answer: A
Explanation:
Explanation/Reference: https://www.researchgate.net/post/
Multicollinearity_issues_is_a_value_less_than_10_acceptable_for_VIF
NEW QUESTION 34
This question will ask you to provide a segment of missing code.
The following code is used to create missing value indicator variables for input variables, fred1 to fred7.
Which segment of code would complete the task?
- A. Option D
- B. Option A
- C. Option C
- D. Option B
Answer: C
NEW QUESTION 35
Which statistic is based on the maximum vertical distance between the primary event EDF and the secondary event EDF?
- A. Max EDF
- B. Brier Score
- C. SBC
- D. KS
Answer: C
NEW QUESTION 36
A predictive model uses a data set that has several variables with missing values.
What two problems can arise with this model? (Choose two.)
- A. There will be a high rate of collinearity among input variables.
- B. The model will likely be overfit.
- C. Complete case analysis means that fewer observations will be used in the model building process.
- D. New cases with missing values on input variables cannot be scored without extra data processing.
Answer: C,D
Explanation:
Explanation/Reference:
NEW QUESTION 37
One common approach for predicting rare events in the LOGISTIC procedure is to build a model that disproportionately over-re presents those cases with an event occurring (e.g. a 50-50 event/non-event split).
What problem does this present?
- A. Only the non-intercept parameter estimates are biased.
- B. All parameter estimates are biased.
- C. Sensitivity estimates are biased.
- D. Only the intercept estimate is biased.
Answer: D
NEW QUESTION 38
An analyst knows that the categorical predictor, storeId, is an important predictor of the target.
However, store_Id has too many levels to be a feasible predictor in the model. The analyst wants to combine stores and treat them as members of the same class level.
What are the two most effective ways to address the problem? (Choose two.)
- A. Use subject matter expertise to combine stores that are similar.
- B. Eliminate store_id as a predictor in the model because it has too many levels to be feasible.
- C. Randomly combine the stores into five groups to keep the stochastic variation among the observations intact.
- D. Cluster by using Greenacre's method to combine stores that are similar.
Answer: A,D
NEW QUESTION 39
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How to Prepare For A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam
Preparation Guide for A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam
Introduction
SAS (previously “Statistical Analysis System”) may be a package suite designed by SAS Institute for advanced analytics, variable analyses, business intelligence, knowledge management, and prognosticative analytics.
A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling exam is administered by SAS and Pearson VUE. has created a track for IT professionals to certify as business analyst on SAS. This certification program provides SAS professionals a way to demonstrate their skills. The assessment is based on a rigorous exam using industry standard methodology to determine whether a candidate meets SAS's proficiency standards.
SAS® Certified Statistical Business Analyst for SAS®9 this credential is for professionals who use SAS/STAT® software to conduct and interpret complex statistical data analysis.
In this guide, we will cover the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Exam, A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certified professional's salary and all aspects of the A00-240: Statistical Business Analysis Using SAS®9:Regression and Modeling Certification.
The SAS international Certification Program currently issues versioned accreditation that don't lapse.The version is indicated in the credential name.
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