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| Section | Weight | Objectives |
|---|
| Topic 1: Analysis of Variance (ANOVA) | 10% | - Assess ANOVA assumptions - Perform post-hoc tests - Use PROC GLM for ANOVA - Analyze factor interactions
|
| Topic 2: Prepare Inputs for Predictive Modeling | 20% | - Identify data preparation challenges - Transform categorical variables - Screen variables with CORR procedure and empirical logit plots - Manipulate data using DATA step
|
| Topic 3: Measure and Evaluate Model Performance | 25% | - Validate models with training/validation data - Create and interpret ROC, lift, and gains charts - Determine optimal decision cut-off values - Apply honest assessment principles - Use confusion matrices and classification metrics
|
| Topic 4: Logistic Regression | 25% | - Interpret odds ratios and model coefficients - Score new datasets with LOGISTIC and PLM - Select and optimize input variables - Build models using PROC LOGISTIC
|
| Topic 5: Linear Regression | 20% | - Fit multiple linear regression models - Conduct residual and diagnostic analysis - Interpret REG, GLM, and PLM procedure output - Perform model selection with GLMSELECT
|
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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q78-Q83):
NEW QUESTION # 78
Which statistic, calculated from a validation sample, can help decide which model to use for prediction of a binary target variable?
- A. Average Squared Error
- B. Chi Square
- C. Mallow's Cp
- D. Adjusted R Square
Answer: A
NEW QUESTION # 79
In order to perform honest assessment on a predictive model, what is an acceptable division between training, validation, and testing data?
- A. Training: 50% Validation: 50% Testing: 0%
- B. Training: 50% Validation: 0% Testing: 50%
- C. Training: 100% Validation: 0% Testing: 0%
- D. Training: 0% Validation: 100% Testing: 0%
Answer: A
NEW QUESTION # 80
After performing an ANOVA test, an analyst has determined that a significant effect exists due to income. The analyst wants to compare each Income to all others and wants to control for experimentwise error.
Which GLM procedure statement would provide the most appropriate output?
- A. lsmeans Income / pdiff=all adjust=tukey;
- B. lsmeans Income / pdiff=control adjust=dunnett;
- C. lsmeans Income / pdiff=control adjust=t;
- D. lsmeans Income / pdiff=all adjust=t;
Answer: B
NEW QUESTION # 81
Given the following LOGISTIC procedure:

What is the difference between the datasets OUTFILEJ and OUTFILE_2?
- A. OUTFILE_1 contains the model goodness of fit statistics while OUTFILE_2 contains the newly scored logits.
- B. OUTFILE_1 contains the model goodness of fit statistics while OUTFILE_2 contains the newly scored probabilities
- C. OUTFILE_1 contains the final parameter estimates while OUTFILE_2 contains the newly scored probabilities.
- D. OUTFILEJ contains the final parameter estimates and Wald Chi-Square values while OUTFILE_2 contains the newly scored probabilities.
Answer: C
NEW QUESTION # 82
Refer to the ROC curve:

As you move along the curve, what changes?
- A. The probability cutoff for scoring
- B. The priors in the population
- C. The true negative rate in the population
- D. The proportion of events in the training data
Answer: A
NEW QUESTION # 83
......
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