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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
