Statistical analysis with SAS

Module 1: Introduction to SAS

1. Overview of SAS:
   • Introduction to SAS software.
   • Installation and setup.
2. SAS Interface:
   • Navigating the SAS interface.
   • Understanding the SAS program structure.

Module 2: Data Import and Preparation

1. Importing Data into SAS:
   • Loading data from different sources (Excel, CSV, databases).
   • Handling missing data and data types.
2. Data Cleaning in SAS:
   • Identifying and handling outliers.
   • Recoding variables and creating new variables.

Module 3: Descriptive Statistics in SAS

1. Descriptive Statistics:
   • Calculating measures of central tendency and dispersion.
   • Generating frequency distributions and summary tables.
2. Graphical Representation:
   • Creating charts and graphs in SAS.
   • Customizing visualizations.

Module 4: Inferential Statistics in SAS

1. Hypothesis Testing:
   • Conducting t-tests, chi-square tests, and ANOVA.
   • Interpreting results.
2. Regression Analysis in SAS:
   • Performing simple and multiple regression.
   • Assessing regression assumptions.

Module 5: Advanced Data Management

1. Data Transformation:
   • Creating computed variables.
   • Reshaping datasets.
2. Combining Datasets:
   • Merging datasets in SAS.
   • Concatenating datasets.

Module 6: Longitudinal Data Analysis

1. Introduction to Longitudinal Data:
   • Understanding longitudinal data concepts.
   • Analyzing and modeling longitudinal data in SAS.
2. Mixed Models:
   • Estimating mixed models for repeated measures.
   • Interpreting results.

Module 7: Survival Analysis in SAS

1. Introduction to Survival Analysis:
   • Understanding survival analysis concepts.
   • Performing survival analysis in SAS.
2. Cox Proportional-Hazards Model:
   • Estimating and interpreting Cox models.
   • Assessing model assumptions.

Module 8: Real-world Applications and Case Studies

1. Case Studies:
   • Applying data management and analysis techniques to real-world scenarios.
   • Analyzing and interpreting results.
2. Capstone Project:
   • Undertaking a comprehensive data analysis project using SAS.
   • Presenting findings and insights.

Additional Considerations:

• Hands-On Exercises and Labs:
  • Practical application of SAS concepts with real datasets.
  • Group exercises and coding labs.
• Interactive Sessions:
  • Q&A sessions and discussions.
  • Peer-to-peer learning activities.
• Resources and Further Learning:
  • Providing additional resources for self-paced learning.
  • Sharing relevant books, online courses, and forums.