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SASInstitute A00-240 Exam Syllabus Topics:

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

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q17-Q22):

NEW QUESTION # 17
A non-contributing predictor variable (Pr > |t| =0.658) is added to an existing multiple linear regression
model.
What will be the result?

A. A decrease in Mean Square Error
B. An increase in R-Square
C. A decrease in R-Square
D. No change in R-Square

Answer: B

NEW QUESTION # 18
Given the following GLM procedure output:

Which statement is correct at an alpha level of 0.05?

A. School should be removed because it is significant.
B. Gender should be removed because it is non-significant.
C. School*Gender should be removed because it is non-significant.
D. Gender should not be removed due to its involvement in the significant interaction.

Answer: D

NEW QUESTION # 19
A company has branch offices in eight regions. Customers within each region are classified as either "High Value" or "Medium Value" and are coded using the variable name VALUE. In the last year, the total amount of purchases per customer is used as the response variable.
Suppose there is a significant interaction between REGION and VALUE. What can you conclude?

A. The difference between average purchases for medium and high value customers depends on the region.
B. Regions with higher average purchases have more medium value customers.
C. Regions with higher average purchases have more high value customers.
D. More high value customers are found in some regions than others.

Answer: A

Explanation:
Explanation/Reference:

NEW QUESTION # 20
CORRECT TEXT
A linear model has the following characteristics:
*A dependent variable (y)
*One continuous variable (xl), including a quadratic term (x12)
*One categorical (d with 3 levels) predictor variable and an interaction term (d by x1)
How many parameters, including the intercept, are associated with this model?
Enter your numeric answer in the space below. Do not add leading or trailing spaces to your answer.

Answer:

Explanation:
7

NEW QUESTION # 21
Refer to the ROC curve:

As you move along the curve, what changes?

A. The priors in the population
B. The proportion of events in the training data
C. The probability cutoff for scoring
D. The true negative rate in the population

Answer: C

NEW QUESTION # 22
......

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