Statistical Data Analysis using SAS Training Course

This course equips participants with practical skills to manage and analyze data using SAS. It focuses on data steps, procedures, descriptive statistics, hypothesis testing, regression, ANOVA, and reporting of statistical results.

Target Groups

• Statisticians and data analysts
• Researchers and postgraduate students
• Health and clinical data analysts
• Monitoring and evaluation analysts
• Banking, insurance, and market analysts
• Government statistics staff
• Managers and supervisors seeking to strengthen organizational practice in this field
• Consultants and professionals supporting related organizations

Course Objectives

By the end of this course, participants will be able to:

• Navigate the SAS environment and program structure
• Import, explore, and manage data with the DATA step
• Use PROC steps for descriptive statistics
• Format, transform, and combine datasets
• Conduct hypothesis tests and chi-square analyses
• Perform correlation and regression analysis
• Apply ANOVA and general linear models
• Fit logistic regression models
• Produce graphs, tables, and reports using ODS
• Write efficient, reusable SAS programs and macros

Course Modules

Module 1: Introduction to SAS

• SAS environment and interfaces
• Program structure
• Libraries and datasets
• Importing data
• Logs and error handling

Module 2: Data Management with the DATA Step

• Creating and modifying variables
• Conditional logic
• Functions
• Sorting and subsetting
• Merging and appending datasets

Module 3: Descriptive Statistics and Exploration

• PROC MEANS, FREQ, and UNIVARIATE
• Cross-tabulations
• Data quality checks
• Formats and labels
• Graphical exploration

Module 4: Hypothesis Testing

• T-tests and nonparametric tests
• Chi-square tests
• Confidence intervals
• Assumption checking
• Interpreting p-values

Module 5: Correlation and Linear Regression

• PROC CORR
• PROC REG
• Model interpretation
• Diagnostics
• Variable selection

Module 6: ANOVA and General Linear Models

• One-way and two-way ANOVA
• PROC GLM
• Post-hoc comparisons
• Interaction effects
• Assumption checks

Module 7: Logistic Regression

• PROC LOGISTIC
• Odds ratios
• Model fit and ROC curves
• Categorical predictors
• Prediction

Module 8: Reporting with ODS and Graphics

• Output Delivery System
• Tables and reports
• SGPLOT graphics
• Exporting results
• Report formatting

Module 9: SAS Macros and Efficient Programming

• Macro variables
• Simple macros
• Loops and automation
• Debugging
• Documentation

Module 10: Practical Exercises and Capstone Project

• Importing and cleaning a sample dataset
• Running descriptive and inferential analyses
• Fitting regression and logistic models
• Producing a formatted report
• Presenting a statistical analysis summary