SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Practice Exam

SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Practice Exam

 

The SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling A00-240 exam validates your ability to use SAS 9 software for statistical analysis, specifically focusing on regression and modeling techniques. It's designed for individuals who want to leverage SAS 9 for data-driven decision making in business settings.

 

Who Should Take This Exam?

This exam is ideal for:

• Business Analysts
• Statisticians/Data Analysts
• Those pursuing a career in Business Intelligence or Data Science

 

Exam Details 

• Exam Code: A00-240
• Exam Name: SAS Statistical Business Analysis Using SAS®9: Regression and Modeling Exam
• Exam Languages: English
• Exam Questions: 60 Questions
• Time: 2 hours
• Passing Score: 68%

 

Exam Course Outline 

The Exam covers the given topics  - 

Domain 1: Overview of ANOVA - 10%

Verify the assumptions of ANOVA

• Explain the central limit theorem and when it must be applied
• Examine the distribution of continuous variables (histogram, box -whisker, Q-Q plots)
• Describe the effect of skewness on the normal distribution
• Define H0, H1, Type I/II error, statistical power, p-value
• Describe the effect of sample size on p-value and power
• Interpret the results of hypothesis testing
• Interpret histograms and normal probability charts
• Draw conclusions about your data from histogram, box-whisker, and Q-Q plots
• Identify the kinds of problems may be present in the data: (biased sample, outliers, extreme values)
• For a given experiment, verify that the observations are independent
• For a given experiment, verify the errors are normally distributed
• Use the UNIVARIATE procedure to examine residuals
• For a given experiment, verify all groups have equal response variance
• Use the HOVTEST option of MEANS statement in PROC GLM to asses response variance

 

Analyze differences between population means using the GLM and TTEST procedures

• Use the GLM Procedure to perform ANOVA
• Evaluate the null hypothesis using the output of the GLM procedure
• Interpret the statistical output of the GLM procedure (variance derived from MSE, F value, p-value R**2, Levene's test)
• Interpret the graphical output of the GLM procedure
• Use the TTEST Procedure to compare means

 

Perform ANOVA post hoc test to evaluate treatment effect

• Use the LSMEANS statement in the GLM or PLM procedure to perform pairwise comparisons
• Use PDIFF option of LSMEANS statement
• Use ADJUST option of the LSMEANS statement (TUKEY and DUNNETT)
• Interpret diffograms to evaluate pairwise comparisons
• Interpret control plots to evaluate pairwise comparisons
• Compare/Contrast use of pairwise T-Tests, Tukey and Dunnett comparison methods

 

Detect and analyze interactions between factors

• Use the GLM procedure to produce reports that will help determine the significance of the interaction between factors. MODEL statement
• LSMEANS with SLICE=option (Also using PROC PLM)
• ODS SELECT
• Interpret the output of the GLM procedure to identify interaction between factors: p-value
• F Value
• R Squared
• TYPE I SS
• TYPE III SS

 

Domain 2: Understand Linear Regression - 20%

Fit a multiple linear regression model using the REG and GLM procedures

• Use the REG procedure to fit a multiple linear regression model
• Use the GLM procedure to fit a multiple linear regression model

 

Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models

• Interpret REG or GLM procedure output for a multiple linear regression model: convert models to algebraic expressions
• Convert models to algebraic expressions
• Identify missing degrees of freedom
• Identify variance due to model/error, and total variance
• Calculate a missing F value
• Identify variable with largest impact to model
• For output from two models, identify which model is better
• Identify how much of the variation in the dependent variable is explained by the model
• Conclusions that can be drawn from REG, GLM, or PLM output: (about H0, model quality, graphics)

 

Use the REG or GLMSELECT procedure to perform model selection

• Use the SELECTION option of the model statement in the GLMSELECT procedure
• Compare the differentmodel selection methods (STEPWISE, FORWARD, BACKWARD)
• Enable ODS graphics to display graphs from the REG or GLMSELECT procedure
• Identify best models by examining the graphical output (fit criterion from the REG or GLMSELECT procedure)
• Assign names to models in the REG procedure (multiple model statements)

 

Assess the validity of a given regression model through the use of diagnostic and residual analysis

• Explain the assumptions for linear regression
• From a set of residuals plots, asses which assumption about the error terms has been violated
• Use REG procedure MODEL statement options to identify influential observations (Student Residuals, Cook's D, DFFITS, DFBETAS)
• Explain options for handling influential observations
• Identify collinearity problems by examining REG procedure output
• Use MODEL statement options to diagnose collinearity problems (VIF, COLLIN, COLLINOINT)

 

Domain 3: Logistic Regression - 25%

Perform logistic regression with the LOGISTIC procedure

• Identify experiments that require analysis via logistic regression
• Identify logistic regression assumptions
• logistic regression concepts (log odds, logit transformation, sigmoidal relationship between p and X)
• Use the LOGISTIC procedure to fit a binary logistic regression model (MODEL and CLASS statements)

 

Optimize model performance through input selection

• Use the LOGISTIC procedure to fit a multiple logistic regression model
• LOGISTIC procedure SELECTION=SCORE option
• Perform Model Selection (STEPWISE, FORWARD, BACKWARD) within the LOGISTIC procedure

 

Interpret the output of the LOGISTIC procedure

• Interpret the output from the LOGISTIC procedure for binary logistic regression models: Model Convergence section
• Testing Global Null Hypothesis table
• Type 3 Analysis of Effects table
• Analysis of Maximum Likelihood Estimates table
• Association of Predicted Probabilities and Observed Responses

 

Score new data sets using the LOGISTIC and PLM procedures

• Use the SCORE statement in the PLM procedure to score new cases
• Use the CODE statement in PROC LOGISTIC to score new data
• Describe when you would use the SCORE statement vs the CODE statement in PROC LOGISTIC
• Use the INMODEL/OUTMODEL options in PROC LOGISTIC
• Explain how to score new data when you have developed a model from a biased sample

 

Domain 4: Prepare Inputs for Predictive Model Performance - 20%

Identify the potential challenges when preparing input data for a model

• Identify problems that missing values can cause in creating predictive models and scoring new data sets
• Identify limitations of Complete Case Analysis
• Explain problems caused by categorical variables with numerous levels
• Discuss the problem of redundant variables
• Discuss the problem of irrelevant and redundant variables
• Discuss the non-linearities and the problems they create in predictive models
• Discuss outliers and the problems they create in predictive models
• Describe quasi-complete separation
• Discuss the effect of interactions
• Determine when it is necessary to oversample data

 

Use the DATA step to manipulate data with loops, arrays, conditional statements and functions

• Use ARRAYs to create missing indicators
• Use ARRAYS, LOOP, IF, and explicit OUTPUT statements

 

Improve the predictive power of categorical inputs

• Reduce the number of levels of a categorical variable
• Explain thresholding
• Explain Greenacre's method
• Cluster the levels of a categorical variable via Greenacre's method using the CLUSTER procedure
• Convert categorical variables to continuous using smooth weight of evidence

 

Screen variables for irrelevance and non-linear association using the CORR procedure

• Explain how Hoeffding's D and Spearman statistics can be used to find irrelevant variables and non-linear associations
• Produce Spearman and Hoeffding's D statistic using the CORR procedure (VAR, WITH statement)
• Interpret a scatter plot of Hoeffding's D and Spearman statistic to identify irrelevant variables and non-linear associations

 

Screen variables for non-linearity using empirical logit plots

• Use the RANK procedure to bin continuous input variables (GROUPS=, OUT= option; VAR, RANK statements)
• Interpret RANK procedure output
• Use the MEANS procedure to calculate the sum and means for the target cases and total events (NWAY option; CLASS, VAR, OUTPUT statements)
• Create empirical logit plots with the SGPLOT procedure
• Interpret empirical logit plots

 

Domain 5: Measure Model Performance - 25%

Apply the principles of honest assessment to model performance measurement

• Explain techniques to honestly assess classifier performance
• Explain overfitting
• Explain differences between validation and test data
• Identify the impact of performing data preparation before data is split

 

Assess classifier performance using the confusion matrix

• Explain the confusion matrix
• Define: Accuracy, Error Rate, Sensitivity, Specificity, PV+, PV-
• Explain the effect of oversampling on the confusion matrix
• Adjust the confusion matrix for oversampling

 

Model selection and validation using training and validation data

• Divide data into training and validation data sets using the SURVEYSELECT procedure
• Discuss the subset selection methods available in PROC LOGISTIC
• Discuss methods to determine interactions (forward selection, with bar and @ notation)
• Create interaction plot with the results from PROC LOGISTIC
• Select the model with fit statistics (BIC, AIC, KS, Brier score)

 

Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection

• Explain and interpret charts (ROC, Lift, Gains)
• Create a ROC curve (OUTROC option of the SCORE statement in the LOGISTIC procedure)
• Use the ROC and ROCCONTRAST statements to create an overlay plot of ROC curves for two or more models
• Explain the concept of depth as it relates to the gains chart

 

Establish effective decision cut-off values for scoring

• Illustrate a decision rule that maximizes the expected profit
• Explain the profit matrix and how to use it to estimate the profit per scored customer
• Calculate decision cutoffs using Bayes rule, given a profit matrix
• Determine optimum cutoff values from profit plots
• Given a profit matrix, and model results, determine the model with the highest average profit