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The A00-240 exam is an assessment of the individual's proficiency in data analysis, regression analysis, and modeling using SAS software. It is intended for those who have an understanding of basic statistical concepts and have a desire to develop their expertise in the SAS software. By attaining this credential, you prove your competency in using SAS tools to perform statistical analysis and modeling, enabling you to provide insights into data that can help you make informed business decisions.
SASInstitute A00-240 certification exam is designed for professionals who want to demonstrate their expertise in statistical analysis using SAS 9 software. SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential certification is intended for individuals who have a strong foundation in statistics and want to validate their skills in regression and modeling techniques. The SASInstitute A00-240 Certification Exam covers a wide range of topics, including data analysis, regression modeling, variable selection, and model assessment.
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To prepare for the SASInstitute A00-240 Certification Exam, candidates should have a solid foundation in statistics and be familiar with SAS software. There are a variety of study materials available, including online courses, textbooks, and practice exams. Candidates may also want to consider attending a SAS training course to gain hands-on experience with the software.
SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q75-Q80):
NEW QUESTION # 75
Refer to the following odds ratio table:

What is a correct interpretation of the estimate?
- A. The odds of the event are 1.142 greater for each one thousand dollar increase in salary.
- B. The probability of the event is 1.142 greater for each one thousand dollar increase in salary.
- C. The probability of the event is 1.142 greater for each one dollar increase in salary.
- D. The odds of the event are 1.142 greater for each one dollar increase in salary.
Answer: A
NEW QUESTION # 76
Refer to the exhibit:

An analyst examined logistic regression models for predicting whether a customer would make a purchase. The ROC curve displayed summarizes the models. Using the selected model and the analyst's decision rule, 25% of the customers who did not make a purchase are incorrectly classified as purchasers.
What can be concluded from the graph?
- A. About 25% of the customers who did make a purchase are correctly classified as making a purchase.
- B. About 85% of the customers who did make a purchase are correctly classified as making a purchase.
- C. About 95% of the customers who did make a purchase are correctly classified as making a purchase.
- D. About 50% of the customers who did make a purchase are correctly classified as making a purchase.
Answer: B
NEW QUESTION # 77
Given the following SAS data set TEST:

Which SAS program is NOT a correct way to create dummy variables?

- A. Option C
- B. Option A
- C. Option D
- D. Option B
Answer: C
NEW QUESTION # 78
A linear model has the following characteristics:
* A dependent variable (y)
* One continuous variable (x1)
* One categorical (1 with 3 levels) predictor variable and an interaction term (1 by x1) How many parameters, including the intercept, will be estimated for this model?
Answer:
Explanation:
Enter your numeric answer in the space below.
7
NEW QUESTION # 79
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 requires extra computational effort and time.
- B. There is no ability to compare the effectiveness of different cleansing methods.
- C. It omits the training (and test) data sets from the benefits of the cleansing methods.
- D. It violates assumptions of the model.
Answer: B
NEW QUESTION # 80
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