A00-240 Valid Test Book - Valid A00-240 Exam Notes

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SASInstitute A00-240 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Logistic Regression	25%	- Interpret and Deploy Models 1. Interpret LOGISTIC procedure output 2. Score new data sets using LOGISTIC and PLM procedures - Develop Logistic Models 1. Optimize model performance through input selection 2. Perform logistic regression using the LOGISTIC procedure
Topic 2: Linear Regression	20%	- Interpret Regression Results 1. Evaluate parameter estimates 2. Interpret REG, PLM and GLM output - Build Regression Models 1. Use REG and GLM procedures 2. Fit multiple linear regression models - Model Selection and Diagnostics 1. Perform model selection using REG or GLMSELECT 2. Conduct residual and diagnostic analysis 3. Assess model validity
Topic 3: ANOVA	10%	- ANOVA Analysis 1. Evaluate treatment effects 2. Detect and analyze factor interactions 3. Analyze differences between population means using GLM and TTEST procedures 4. Perform post hoc tests - ANOVA Concepts and Assumptions 1. Identify appropriate ANOVA methods 2. Verify ANOVA assumptions
Topic 4: Prepare Inputs for Predictive Model Performance	20%	- Data Preparation 1. Manipulate data using DATA step programming 2. Identify challenges in preparing model input data 3. Use loops, arrays, functions and conditional statements - Variable Engineering and Screening 1. Use empirical logit plots for nonlinearity detection 2. Improve predictive power of categorical variables 3. Identify nonlinear associations using CORR procedure 4. Screen variables for irrelevance
Topic 5: Measure Model Performance	25%	- Decision Optimization 1. Establish effective decision cutoff values 2. Evaluate scoring strategies - Performance Visualization 1. Create and interpret gains charts 2. Create and interpret lift charts 3. Create and interpret ROC charts - Model Assessment 1. Apply principles of honest assessment 2. Assess classifier performance using confusion matrices - Validation and Selection 1. Compare and select predictive models 2. Perform model validation using training and validation data

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SASInstitute SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Sample Questions (Q21-Q26):

NEW QUESTION # 21
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 R-Square
B. A decrease in Mean Square Error
C. An increase in R-Square
D. No change in R-Square

Answer: C

NEW QUESTION # 22
The SAS data set RESULT contains the following variables:
* Region (GrpA or GrpB)
* Sales (dollars per year)
Which SAS programs can be used to find the p-value for comparing GrpA sales with GrpB sales? (Choose two.)

Answer: B,C

Explanation:
Explanation

NEW QUESTION # 23
Refer to the exhibit:

The plots represent two models, A and B, being fit to the same two data sets, training and validation.
Model A is 90.5% accurate at distinguishing blue from red on the training data and 75.5% accurate at doing the same on validation data. Model B is 83% accurate at distinguishing blue from red on the training data and
78.3% accurate at doing the same on the validation data.
Which of the two models should be selected and why?

A. Model A. It performs better on the boundary for the training data.
B. Model B. It is simpler with a higher accuracy than model A on validation data.
C. Model A. It is more complex with a higher accuracy than model B on training data.
D. Model B. It is more complex with a higher accuracy than model A on validation data.

Answer: B

NEW QUESTION # 24
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 it shows less over fit than Candidate 1
D. Candidate 2, because the area under the curve is greater

Answer: D

NEW QUESTION # 25
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. Rank-ordered values of the variables
B. Weighted sum of chi-square statistics for 2x2 tables
C. Logit link (log(p/1-p))
D. Concordant and discordant pairs of ranked observations

Answer: A

NEW QUESTION # 26
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