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SASInstitute A00-240 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prepare Inputs for Predictive Modeling	20%	- Identify data preparation challenges - Screen variables with CORR procedure and empirical logit plots - Manipulate data using DATA step - Transform categorical variables
Topic 2: Measure and Evaluate Model Performance	25%	- Apply honest assessment principles - Determine optimal decision cut-off values - Use confusion matrices and classification metrics - Validate models with training/validation data - Create and interpret ROC, lift, and gains charts
Topic 3: Linear Regression	20%	- Perform model selection with GLMSELECT - Interpret REG, GLM, and PLM procedure output - Fit multiple linear regression models - Conduct residual and diagnostic analysis
Topic 4: Logistic Regression	25%	- Build models using PROC LOGISTIC - Score new datasets with LOGISTIC and PLM - Interpret odds ratios and model coefficients - Select and optimize input variables
Topic 5: Analysis of Variance (ANOVA)	10%	- Perform post-hoc tests - Assess ANOVA assumptions - Use PROC GLM for ANOVA - Analyze factor interactions

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q63-Q68):

NEW QUESTION # 63
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 a problem since all variance inflation values are less than 10.
B. Collinearity is not a problem since all variance inflation values are less than 10.
C. Collinearity is a problem since all Pr>|t| values are less than 0.05.
D. Collinearity is not a problem since all Pr>|t| values are less than 0.05.

Answer: D

NEW QUESTION # 64
Refer to the exhibit:

The box plot was used to analyze daily sales data following three different ad campaigns.
The business analyst concludes that one of the assumptions of ANOVA was violated.
Which assumption has been violated and why?

A. Constant variance, because Prob > F < .0001.
B. Constant variance, because the interquartile ranges are different in different ad campaigns.
C. Normality, because the interquartile ranges are different in different ad campaigns.
D. Normality, because Prob > F < .0001.

Answer: B

NEW QUESTION # 65
Suppose training data are oversampled in the event group to make the number of events and nonevents roughly equal. A logistic regression is run and the probabilities are output to a data set NEW and given the variable name PE. A decision rule considered is, "Classify data as an event if probability is greater than 0.5." Also the data set NEW contains a variable TG that indicates whether there is an event (1=Event, 0= No event).
The following SAS program was used.

What does this program calculate?

A. Sensitivity
B. Depth
C. Positive predictive value
D. Specificity

Answer: A

NEW QUESTION # 66
Refer to the exhibit.

These graphs were created using the GLM procedure with the plots(only)=diagnosticsoption.
Which plot do you use to identify influential observations?

A. Fit - Mean and Residual Plot
B. Cook's D by Observation
C. Residual by Predicted
D. Residual by Quantile

Answer: B

Explanation:
Explanation/Reference: https://books.google.com.pk/books?id=fx77Pzerty8C&pg=PA279&lpg=PA279&dq=sas+statistical
+analysis+plot+do+you+use+to+identify+influential
+observations&source=bl&ots=AJavSAql_g&sig=ACfU3U0jbAa6lTWvSeSJ6y5bwp4osAyigA&hl=en&sa=X&ve d=2ahUKEwje34nyqajnAhXDThUIHUfUCj0Q6AEwEXoECAoQAQ#v=onepage&q=sas%20statistical%
20analysis%20plot%20do%20you%20use%20to%20identify%20influential%20observations&f=false

NEW QUESTION # 67
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 zero mean and constant variance.
B. The errors are independent, normally distributed with zero mean and constant variance.
C. The errors are independent, normally distributed with constant mean and zero variance.
D. The errors are correlated, normally distributed with constant mean and zero variance.

Answer: B

NEW QUESTION # 68
......

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