Statistical Business Analyst Using SAS 9
SAS Certified Professional Exam Guide
1 Exam Overview
The SAS Certified Statistical Business Analyst Using SAS 9 certification validates your ability to manipulate and analyze data using SAS procedures and the SAS Enterprise Guide interface.
Exam Code: A00-240
Duration: 2 hours
Questions: 60-65 multiple choice questions
Passing Score: 68%
Prerequisites: None (SAS programming experience recommended)
2 Exam Content Areas
2.1 1. Access and Manipulate Data (30%)
2.1.1 Import and Export Data
- Import various data formats
- Export data to different formats
- Use PROC IMPORT and PROC EXPORT
/* Example: Importing Excel data */
PROC IMPORT DATAFILE="/path/to/sales.xlsx"
OUT=work.sales
DBMS=XLSX
REPLACE;
SHEET="Q1_Sales";
GETNAMES=YES;
RUN;2.1.2 Manipulate Data
- Create new variables
- Subset and filter data
- Sort and organize data
/* Example: Data manipulation */
DATA work.analyzed;
SET work.sales;
/* Create calculated fields */
profit = revenue - cost;
profit_margin = (profit / revenue) * 100;
/* Categorize data */
IF profit_margin >= 20 THEN category = 'High';
ELSE IF profit_margin >= 10 THEN category = 'Medium';
ELSE category = 'Low';
FORMAT profit_margin PERCENT8.2;
RUN;2.2 2. Prepare Data for Analysis (25%)
2.2.1 Investigate and Summarize Data
- Examine data distributions
- Identify outliers and missing values
- Calculate descriptive statistics
/* Example: Data investigation */
PROC MEANS DATA=work.sales N MEAN STD MIN MAX MEDIAN;
VAR revenue cost profit;
CLASS region;
RUN;
PROC UNIVARIATE DATA=work.sales;
VAR profit_margin;
HISTOGRAM profit_margin;
PROBPLOT profit_margin;
RUN;2.2.2 Clean and Prepare Data
- Handle missing values
- Identify and treat outliers
- Transform variables
/* Example: Data cleaning */
DATA work.cleaned;
SET work.sales;
/* Handle missing values */
IF MISSING(revenue) THEN DELETE;
/* Replace missing with mean */
IF MISSING(cost) THEN cost = 5000;
/* Identify outliers */
IF profit < -10000 OR profit > 100000 THEN outlier_flag = 1;
ELSE outlier_flag = 0;
RUN;2.3 3. Analyze Data (45%)
2.3.1 Generate Frequency Tables
- Create one-way and two-way frequency tables
- Calculate percentages and cumulative frequencies
- Perform chi-square tests
/* Example: Frequency analysis */
PROC FREQ DATA=work.sales;
TABLES region product / NOCUM NOPERCENT;
TABLES region*product / CHISQ;
RUN;2.3.2 Generate Summary Statistics
- Calculate measures of central tendency and dispersion
- Create grouped summaries
- Generate custom statistics
/* Example: Summary statistics */
PROC MEANS DATA=work.sales MEAN STD MIN MAX SUM;
VAR revenue profit;
CLASS region product;
OUTPUT OUT=work.summary
MEAN=avg_revenue avg_profit
SUM=total_revenue total_profit;
RUN;2.3.3 Correlation Analysis
- Calculate correlation coefficients
- Create correlation matrices
- Interpret correlation results
/* Example: Correlation analysis */
PROC CORR DATA=work.sales PLOTS=MATRIX;
VAR revenue cost profit marketing_spend;
RUN;
PROC CORR DATA=work.sales NOSIMPLE;
VAR profit;
WITH revenue marketing_spend;
RUN;2.3.4 Simple Linear Regression
- Fit simple linear regression models
- Interpret regression output
- Assess model fit
/* Example: Linear regression */
PROC REG DATA=work.sales;
MODEL profit = revenue / CLB;
PLOT profit*revenue;
RUN;
QUIT;
/* Using PROC GLM */
PROC GLM DATA=work.sales PLOTS=ALL;
MODEL profit = revenue;
OUTPUT OUT=work.predictions PREDICTED=pred_profit RESIDUAL=resid;
RUN;
QUIT;2.3.5 ANOVA (Analysis of Variance)
- One-way ANOVA
- Two-way ANOVA
- Multiple comparisons
/* Example: One-way ANOVA */
PROC ANOVA DATA=work.sales;
CLASS region;
MODEL profit = region;
MEANS region / TUKEY;
RUN;
QUIT;
/* Example: Two-way ANOVA */
PROC GLM DATA=work.sales;
CLASS region product;
MODEL profit = region product region*product;
LSMEANS region product / ADJUST=TUKEY;
RUN;
QUIT;2.3.6 Multiple Regression
- Fit multiple regression models
- Select variables
- Validate assumptions
/* Example: Multiple regression */
PROC REG DATA=work.sales;
MODEL profit = revenue marketing_spend employees /
VIF
SELECTION=STEPWISE
SLS=0.05
SLE=0.10;
PLOT RESIDUAL.*PREDICTED.;
RUN;
QUIT;2.3.7 Logistic Regression
- Binary logistic regression
- Interpret odds ratios
- Assess model performance
/* Example: Logistic regression */
PROC LOGISTIC DATA=work.customers PLOTS=ALL;
MODEL purchased(EVENT='1') = age income previous_purchases /
LACKFIT
CTABLE
RSQUARE;
UNITS age = 10 income = 1000;
OUTPUT OUT=work.scored PRED=predicted_prob;
RUN;2.3.8 Time Series Analysis
- Plot time series data
- Calculate trends
- Apply forecasting methods
/* Example: Time series analysis */
PROC TIMESERIES DATA=work.monthly_sales
PLOT=SERIES
OUT=work.ts_output;
ID date INTERVAL=MONTH;
VAR sales;
RUN;
PROC FORECAST DATA=work.monthly_sales
METHOD=STEPAR
LEAD=12
OUT=work.forecasted;
ID date INTERVAL=MONTH;
VAR sales;
RUN;3 SAS Enterprise Guide
3.1 Interface Overview
SAS Enterprise Guide provides a point-and-click interface for:
- Data access and manipulation
- Statistical analysis
- Report generation
- Task automation
3.2 Common Tasks
3.2.1 Data Import Task
- File → Import Data
- Select data source
- Configure import options
- Review and run
3.2.2 Query Builder
- Visual interface for data manipulation
- Drag-and-drop column selection
- Filter and sort data
- Join tables
3.2.3 Statistical Tasks
Access via: Tasks → Statistics
- Summary Statistics
- Distribution Analysis
- Correlation Analysis
- Regression
- ANOVA
3.3 Project Organization
- Organize tasks in process flow
- Add notes and documentation
- Save projects for reuse
- Share projects with team
4 Study Resources
4.1 Official SAS Resources
- Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression
- Predictive Modeling Using Logistic Regression
- SAS Enterprise Guide 1: Querying and Reporting
4.2 Practice Tips
- Understand Statistical Concepts
- Know when to use each analysis method
- Understand assumptions and limitations
- Interpret statistical output correctly
- Practice with SAS Enterprise Guide
- Familiarize yourself with the interface
- Practice creating process flows
- Learn keyboard shortcuts
- Focus on Interpretation
- Understanding output is crucial
- Practice explaining results
- Know practical significance vs. statistical significance
- Real Data Practice
- Work with diverse datasets
- Handle messy, real-world data
- Practice complete analysis workflows
5 Common Analysis Scenarios
5.1 Scenario 1: Customer Segmentation
/* Analyze customer purchase patterns */
PROC FREQ DATA=work.customers;
TABLES age_group*purchase_category / CHISQ;
RUN;
PROC MEANS DATA=work.customers;
CLASS age_group;
VAR total_purchases average_order_value;
RUN;5.2 Scenario 2: Sales Prediction
/* Predict sales based on marketing spend */
PROC REG DATA=work.campaigns;
MODEL sales = marketing_spend advertising_reach / CLM;
PLOT sales*marketing_spend;
RUN;
QUIT;5.3 Scenario 3: A/B Testing
/* Compare two marketing campaigns */
PROC TTEST DATA=work.campaigns;
CLASS campaign_version;
VAR conversion_rate;
RUN;6 Common Pitfalls
- Ignoring assumptions - Always check regression/ANOVA assumptions
- Misinterpreting p-values - Understand what they really mean
- Over-relying on automation - Understand what tasks are doing
- Not checking data quality - Always investigate data before analysis
7 Sample Questions
7.1 Question 1
What test should you use to determine if there’s a relationship between two categorical variables?
Answer: Chi-square test of independence (using PROC FREQ with CHISQ option)
7.2 Question 2
In linear regression output, what does R-square represent?
Answer: The proportion of variance in the dependent variable explained by the independent variable(s). Values range from 0 to 1, with higher values indicating better model fit.
8 Next Steps
After certification:
- Apply statistical analysis in business contexts
- Learn advanced analytics (machine learning, data mining)
- Consider SAS Visual Analytics certification
- Develop domain-specific expertise