SAS Statistical Business Analyst Quick Facts (2025)

Exam Domain Breakdown

Domain 1: ANOVA (10% of the exam)

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 data from histogram, box-whisker, and Q-Q plots
• Identify problems present in data such as biased samples or outliers
• Verify independence of observations and normality of errors
• Use the UNIVARIATE procedure to examine residuals
• Verify equal response variance among groups
• Use the HOVTEST option in the MEANS statement in PROC GLM

Summary:
This section develops understanding of the foundational assumptions required for valid ANOVA results. Candidates learn how to visually and statistically check distributions, residuals, and variance equality using SAS procedures. By mastering these checks, you ensure reliable analysis results that support meaningful interpretation of data comparisons.

You will also practice verifying assumptions through SAS OUTPUT and UNIVARIATE procedures, interpreting histograms, and assessing variance homogeneity. This lays the groundwork for accurate use of ANOVA in data-driven conclusions and supports higher-level modeling accuracy across other analytical domains.

Analyze differences between population means using the GLM and TTEST procedures

• Use the GLM Procedure (CLASS, MODEL, MEANS, OUTPUT statements)
• Evaluate the null hypothesis with GLM output
• Interpret statistical output: MSE, F value, p-value, R-squared, Levene’s test
• Interpret graphical output from GLM
• Use the TTEST Procedure to compare means

Summary:
Here you’ll learn to apply the GLM and TTEST procedures to assess differences in population means. The focus is on connecting statistical output values to real-world interpretations, such as variance and significance understanding that aids decision-making.

Interpreting p-values, F-statistics, and visualization outputs teaches you how to validate or reject hypotheses with confidence. Through these exercises, proficiency in identifying true group differences and their business implications grows stronger.

Perform ANOVA post hoc test to evaluate treatment effect

• Use the LSMEANS statement in GLM or PLM
• Apply PDIFF and ADJUST options (TUKEY and DUNNETT)
• Interpret diffograms and control plots
• Compare pairwise T-Tests, Tukey, and Dunnett methods

Summary:
This section highlights post hoc testing techniques that extend ANOVA conclusions by pinpointing which specific group means differ. You will learn the value of adjusting for multiple comparisons using techniques like Tukey and Dunnett.

By understanding when to apply these options and how to interpret diffograms and control plots, you gain confidence in drawing clear, accurate conclusions about treatment contrasts and their practical relevance.

Detect and analyze interactions between factors

• Use the GLM procedure with MODEL statements
• Apply LSMEANS with SLICE options or PROC PLM
• Interpret output to identify interaction effects using F values and R-squared
• Evaluate TYPE I and TYPE III sums of squares

Summary:
This section explores how multiple factors work together to influence outcomes. You will use GLM and PLM procedures to reveal interaction effects and interpret sum of squares output to evaluate factor relationships.

Developing these skills enables you to uncover complex patterns in data and communicate nuanced analytical insights that account for more than one influencing factor, aligning advanced SAS analysis to practical scenarios.

Domain 2: Linear Regression (20% of the exam)

Fit a multiple linear regression model using REG and GLM procedures

• Use PROC REG and PROC GLM to fit multiple regression models

Summary:
This module focuses on building predictive models using multiple linear regression within SAS. You’ll gain experience specifying model statements in both REG and GLM procedures to quantify relationships between continuous predictors and outcomes.

Applying these techniques helps you transform business questions into analytical models that predict results with precision and clarity.

Analyze output from REG, PLM, and GLM for multiple linear regression models

• Convert models to algebraic expressions
• Identify missing degrees of freedom and variance components
• Calculate F values and compare models
• Interpret influence of variables from output statistics
• Draw conclusions about model fit using R-squared and p-values

Summary:
Mastering output interpretation enables you to connect statistical values to actionable conclusions. Understanding degree of freedom adjustments, model quality indicators, and their impact ensures your models are both scientifically sound and business-relevant.

This section emphasizes using SAS output metrics to judge data relationships objectively, allowing for clear communication of regression results across technical and non-technical teams.

Use REG or GLMSELECT for model selection

• Apply SELECTION options, including STEPWISE, FORWARD, and BACKWARD
• Use ODS graphics to visualize selection results
• Evaluate model fit criteria to identify optimal models

Summary:
You’ll learn to refine regression models for optimal accuracy and interpretability by applying model selection methods. Understanding stepwise and other automated selection options helps reveal which variables matter most.

Practical exposure to ODS graphics strengthens your ability to visually validate model selection outcomes and make data-driven adjustments strategically.

Assess regression validity through diagnostic and residual analysis

• Explain regression assumptions
• Identify assumption violations using residual plots
• Use MODEL options to find influential observations
• Detect collinearity problems with VIF and COLLIN options

Summary:
In this stage, you will focus on ensuring your regression models are statistically valid. Identifying influential data points and addressing multicollinearity ensures results that truly represent underlying data relationships.

SAS diagnostic tools empower you to locate and correct model weaknesses, fostering high-fidelity predictive performance in diverse datasets.

Domain 3: Logistic Regression (25% of the exam)

Perform logistic regression with the LOGISTIC procedure

• Identify logistic regression use cases and assumptions
• Understand core concepts like log odds and logit transformations
• Fit binary logistic models using PROC LOGISTIC

Summary:
This section introduces logistic regression fundamentals for binary outcome analysis. By working with SAS procedures, you’ll model event probabilities and interpret logit relationships effectively.

Understanding these fundamentals equips analysts to evaluate risk, classify behavior, and make robust data-guided business recommendations.

Optimize model performance through input selection

• Use SELECTION=SCORE, STEPWISE, FORWARD, BACKWARD in PROC LOGISTIC

Summary:
In this part, you’ll explore variable selection strategies that enhance logistic model accuracy and efficiency. Learning when to include or exclude predictors leads to leaner, more interpretable models.

Using SAS’s selection capabilities ensures models that balance performance and simplicity, vital for fast, clear reporting in business settings.

Interpret output from the LOGISTIC procedure

• Review Model Convergence and Type 3 Analysis sections
• Understand Global Null Hypothesis and Maximum Likelihood Estimates tables
• Analyze predicted vs. observed probabilities

Summary:
Interpreting logistic regression output transforms statistical results into precise, meaningful conclusions. You’ll learn how to validate convergence, assess overall model significance, and uncover the effects of individual predictors.

This ability improves your analytical storytelling, making outcome probabilities clear and impactful for decision-makers.

Score new datasets

• Use SCORE and CODE statements in PROC LOGISTIC or PLM
• Apply INMODEL/OUTMODEL options for model portability
• Handle scoring with biased samples

Summary:
Here, you’ll translate models into operational scoring processes that apply predictive insights to new observations. SAS tools like SCORE and CODE streamline real-world deployment.

By understanding portability and bias adjustments, you can ensure accurate scoring models that maintain reliability across varied applications.

Domain 4: Prepare Inputs for Predictive Model Performance (20% of the exam)

Identify potential challenges when preparing input data

• Recognize issues with missing values, redundant variables, or high-cardinality categorical inputs
• Manage nonlinearities, outliers, quasi-complete separation, and oversampling needs

Summary:
This section builds awareness of the structured preparation steps that lead to reliable models. Recognizing data issues early simplifies modeling and improves interpretability downstream.

By mastering data hygiene concepts, you develop the intuition to prevent pitfalls that can distort predictive results or inflate apparent accuracy.

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

• Apply ARRAYS, LOOP, IF, and OUTPUT statements

Summary:
This practical area reinforces SAS programming fluency for transforming data effectively. By utilizing arrays and conditional logic, you can create missing flags or generate structured data manipulations.

Such capabilities elevate model readiness, ensuring consistent input quality for any advanced analytic process.

Improve predictive power of categorical inputs

• Reduce category levels through clustering or thresholding
• Apply Greenacre’s method via PROC CLUSTER
• Convert categorical variables to continuous using weight of evidence

Summary:
This section focuses on enhancing data representation quality. Transforming categories into information-rich, statistically sound features drives model precision and interpretability.

You’ll apply clustering techniques and weighting schemes that uncover meaningful structure, aligning complex business variables with numeric modeling requirements.

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

• Use Hoeffding’s D and Spearman statistics to locate irrelevant predictors
• Interpret scatter plots of association measures

Summary:
Here, you’ll explore correlation-based filters to determine input relevance. Leveraging statistical associations helps fine-tune data inputs for optimal model clarity.

By learning to interpret correlation visualizations, you can quickly isolate valuable predictive signals from noise, ensuring model inputs are both efficient and accurate.

Screen variables for non-linearity using empirical logit plots

• Use PROC RANK and PROC MEANS to create bins
• Generate empirical logit plots with SGPLOT and interpret patterns

Summary:
This section enhances your ability to visualize and adjust for nonlinearity in predictive variables. By ranking and plotting input features, you uncover patterns that inform better model transformations.

The integration of SAS plotting tools provides both statistical evidence and visual insight, supporting sharper accuracy across logistic and regression models alike.

Domain 5: Measure Model Performance (25% of the exam)

Apply honest assessment principles to model performance

• Describe validation vs. test data roles
• Understand overfitting and data preparation effects

Summary:
This section emphasizes assessing model performance under realistic conditions. You’ll practice creating validation strategies that confirm your model’s predictive reliability on unseen data.

Developing strong assessment habits ensures models that generalize well, maintaining credibility and value in production use.

Assess classifier performance using the confusion matrix

• Define accuracy, sensitivity, specificity, and predictive values
• Adjust confusion matrices for oversampling

Summary:
Through hands-on application of confusion matrices, you’ll evaluate classifier strengths and weaknesses quantitatively. Each metric reveals a different facet of performance that aids practical model interpretation.

Adjusting for oversampling enables fair comparison between models, strengthening decision-making around deployment readiness.

Model selection and validation using training and validation sets

• Use SURVEYSELECT for sampling
• Apply PROC LOGISTIC for subset selection and interaction checks
• Compare models with fit statistics (BIC, AIC, KS, Brier score)

Summary:
Building and validating models requires an analytical approach to sampling and fit evaluation. This section refines your understanding of how selection methods influence predictive outcomes.

By emphasizing fit statistics and validation splits, it ensures you can choose the most stable, accurate model in practice.

Create and interpret ROC, lift, and gains charts

• Use ROC and ROCCONTRAST statements and OUTROC options
• Compare model performance visually

Summary:
This module enhances visual performance interpretation skills through specialized charts. ROC and lift curves offer clear graphical insights into tradeoffs between precision and recall.

Learning to use these plots correctly helps communicate performance findings seamlessly to both analytical peers and executives.

Establish effective decision cut-off values for scoring

• Apply decision rules and profit matrices
• Calculate Bayes-based optimal cutoffs
• Compare models by expected profit outcomes

Summary:
This final section blends analytics and business impact by aligning model thresholds with profitability measures. You’ll practice setting optimal decision points that maximize return from predictive scoring systems.

By quantifying financial outcomes of model choices, you gain a full performance perspective—from statistical accuracy to strategic value.