ADVANCED SAS

In this lession you will be able to learn the introduction to sas sql and differences. A deep clinical logical flow techniques will be taught of handling realtime data.

In SAS, the PROC SQL SELECT statement retrieves data from one or more SAS datasets (tables), similar to SQL in other environments

In this lesson you will learn how to join 2 or more tables from different external databases using ER Model technqiues . 

A subquery is a query within another query. It is enclosed in parentheses and is used to return a single value or a set of values that the main (outer) query uses for further processing.

Data aggregation and summarization involve combining data to produce summary statistics (like totals, counts, averages) based on groups or conditions.

Creating and managing tables refers to the ability to define, modify, and control datasets (tables) using PROC SQL.

Data transformation refers to modifying, reshaping, or deriving new data from existing datasets using SQL logic in PROC SQL.

Advanced SQL techniques involve using powerful and complex features of SQL to perform sophisticated data manipulation, analysis, and optimization tasks in SAS.

SAS Macros provide a powerful way to automate repetitive tasks, make code dynamic, and simplify complex programs by using variables and reusable code blocks.

Macro variables are symbolic names that hold values and are used to make SAS programs dynamic and flexible.

Macro functions and quoting functions enhance the power of the SAS macro language by allowing manipulation of text, resolving macro variables, and handling special characters safely.

Macro programs are reusable blocks of code in SAS that are defined using %MACRO and ended with %MEND. They allow you to automate tasks, pass parameters, and build dynamic, flexible programs.

Dynamic code generation allows SAS programs to automatically build and execute code at runtime based on input values, conditions, or metadata. It's a powerful feature of the SAS macro language used for automation and scalability.

🐞 Debugging & Error Handling in SAS Macros

Debugging and error handling in SAS macros are essential practices to identify, trace, and fix issues during macro execution. Macros can be complex, and understanding how to track problems ensures accurate and reliable code.

Advanced macro techniques go beyond basic automation and enable highly dynamic, metadata-driven, and scalable programming. These techniques are essential for building reusable and efficient frameworks in large projects.

Introduction to sas graphs

The PROC SGPLOT procedure in SAS is used to create simple and effective visualizations of your data using a wide range of plot types.

Customization in PROC SGPLOT allows you to enhance the visual appeal, clarity, and professionalism of your graphs by modifying their appearance.

Combining plots allows you to visualize multiple relationships or comparisons in a single layout, helping in clearer insights and storytelling.

Statistical graphs go beyond basic plotting by adding analytical elements to the visualization, such as trend lines, confidence bands, or regression models. These enhancements help in interpreting and communicating statistical results visually.

Exporting and sharing graphs in SAS ensures your visualizations can be used in reports, presentations, or external tools. SAS provides multiple options to save and distribute graphs in high-quality formats.

Creating effective visualizations in SAS involves more than just plotting data — it requires attention to detail, clarity, and purpose. Here are some common mistakes to avoid and best practices to follow:

Statistics in SAS involves using built-in procedures to summarize, analyze, and interpret data. SAS offers powerful statistical tools to perform both basic and advanced analyses, making it a key platform for data-driven decision-making.

Descriptive statistics provide a summary of the main features of a dataset, giving insight into its central tendency, variability, and distribution.

Data visualization for statistics involves graphically presenting statistical information to make patterns, trends, and relationships easier to understand and communicate.

🧪 Hypothesis Testing Basics in SAS

Hypothesis testing is a statistical method used to make decisions or inferences about a population based on sample data. It helps determine whether a claim or assumption about a population parameter is likely to be true.

📊 Comparing Groups in SAS Statistics – Description Only

Comparing groups in SAS involves analyzing differences between two or more sets of observations to determine if those differences are statistically significant. This is essential in clinical trials, surveys, and experimental studies.

🔗 Correlation & Regression Basics in SAS – Description Only

Correlation and regression are fundamental statistical tools used in SAS to explore and model relationships between variables.