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A00-240 SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Exam

February 5, 2020 by admin

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Regression and Modeling
Designed for SAS professionals who use SAS/STAT® software to conduct and interpret complex statistical data analysis
Successful candidates should have experience in:
Analysis of variance.
Linear and logistic regression.
Preparing inputs for predictive models.
Measuring model performance.

ANOVA – 10%
Verify the assumptions of ANOVA
Analyze differences between population means using the GLM and TTEST procedures
Perform ANOVA post hoc test to evaluate treatment effect
Detect and analyze interactions between factors

Linear Regression – 20%
Fit a multiple linear regression model using the REG and GLM procedures
Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
Use the REG or GLMSELECT procedure to perform model selection
Assess the validity of a given regression model through the use of diagnostic and residual analysis

Logistic Regression – 25%
Perform logistic regression with the LOGISTIC procedure
Optimize model performance through input selection
Interpret the output of the LOGISTIC procedure
Score new data sets using the LOGISTIC and PLM procedures

Prepare Inputs for Predictive Model Performance – 20%
Identify the potential challenges when preparing input data for a model
Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
Improve the predictive power of categorical inputs
Screen variables for irrelevance and non-linear association using the CORR procedure
Screen variables for non-linearity using empirical logit plots

Measure Model Performance – 25%
Apply the principles of honest assessment to model performance measurement
Assess classifier performance using the confusion matrix
Model selection and validation using training and validation data
Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
Establish effective decision cut-off values for scoring

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
o CLASS statement
o MODEL statement
o MEANS statement
o OUTPUT statement

 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

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 different model 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)

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

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

o METHOD=WARD option
o FREQ, VAR, ID statement
o Use of ODS output to create an output data set

 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

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

Note: All 22 main objectives will be tested on every exam. The 126 expanded objectives are provided for additional explanation and define the entire domain that could be tested.

QUESTION 1
When mean imputation is performed on data after the data is partitioned for honest assessment, what is the most appropriate method for handling the mean imputation?

A. The sample means from the validation data set are applied to the training and test data sets.
B. The sample means from the training data set are applied to the validation and test data sets.
C. The sample means from the test data set are applied to the training and validation data sets.
D. The sample means from each partition of the data are applied to their own partition.

Correct Answer: B

QUESTION 2
An analyst generates a model using the LOGISTIC procedure. They are now interested in getting the
sensitivity and specificity statistics on a validation data set for a variety of cutoff values.
Which statement and option combination will generate these statistics?

A. Score data=valid1 out=roc;
B. Score data=valid1 outroc=roc;
C. mode1 resp(event= ‘1’) = gender region/outroc=roc;
D. mode1 resp(event”1″) = gender region/ out=roc;

Correct Answer: B

QUESTION 3
In partitioning data for model assessment, which sampling methods are acceptable? (Choose two.)
A. Simple random sampling without replacement
B. Simple random sampling with replacement
C. Stratified random sampling without replacement
D. Sequential random sampling with replacement
Correct Answer: AC

QUESTION 4
An analyst has a sufficient volume of data to perform a 3-way partition of the data into training, validation, and test sets to perform honest assessment during the model building process.
What is the purpose of the training data set?

A. To provide an unbiased measure of assessment for the final model.
B. To compare models and select and fine-tune the final model.
C. To reduce total sample size to make computations more efficient.
D. To build the predictive models.

Correct Answer: A

QUESTION 5
The total modeling data has been split into training, validation, and test data. What is the best data to use for model assessment?

A. Training data
B. Total data
C. Test data
D. Validation data

Correct Answer: D

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