Statistical Analysis System SAS

Overview:

Introduction:

Statistical Analysis System (SAS) is a powerful software suite used for data management, advanced analytics, statistical analysis, and predictive modeling. With its robust set of features, SAS is used across industries for data manipulation, reporting, and complex statistical procedures. This training program offers comprehensive knowledge on SAS, from basic functions to advanced statistical modeling. Participants will learn to work with data using SAS, apply statistical techniques, and use SAS to derive meaningful insights from complex datasets. 

Program Objectives:

At the end of this program, participants will be able to:

• Identify the fundamentals of SAS and its components.

• Import, manage, and manipulate data using SAS.

• Perform basic and advanced statistical analysis.

• Generate reports and create visualizations to communicate findings.

• Develop and use predictive models using SAS.

Targeted Audience:

• Data analysts and business analysts.

• Statisticians and researchers.

• Professionals working with data management and analysis.

• Employees looking to gain expertise in SAS for analytics.

• Professionals aiming to enhance their data analysis skills.

Program Outline:

Unit 1:

Introduction to SAS and Data Management:

• Overview of SAS: components and applications in data analysis.

• SAS environment and interface: SAS Studio and SAS Enterprise Guide.

• Data input and output techniques: importing data from various sources: Excel, CSV, and databases.

• Basic data manipulation: sorting, filtering, merging, and transforming data.

• Data cleaning and preparation techniques for analysis.

Unit 2:

Basic Statistical Analysis in SAS:

• Descriptive statistics: mean, median, mode, variance, standard deviation.

• Exploring distributions: normal distribution, binomial, and other probability distributions.

• Hypothesis testing: t-tests, chi-square tests, ANOVA, and p-values.

• Correlation analysis and regression analysis for relationships between variables.

• Statistical procedures in SAS: PROC FREQ, PROC MEANS, and PROC UNIVARIATE.

Unit 3:

Advanced Statistical Procedures:

• Multivariate analysis: multiple regression, principal component analysis (PCA), factor analysis.

• Time series analysis and forecasting techniques.

• Survival analysis and Kaplan-Meier estimation process.

• Logistic regression and generalized linear models (GLM).

• How to use SAS for non-parametric tests and robust statistical methods.

Unit 4:

Data Visualization in SAS:

• Overview of SAS graphics: creating effective charts and plots.

• How to use PROC SGPLOT and PROC GCHART for creating visualizations.

• Customizing graphs: labels, titles, legends, and colors.

• Methods for creating statistical graphics: histograms, box plots, scatter plots, and bar charts.

• Visualizing statistical results to improve report readability and data communication.

Unit 5:

Predictive Modeling and Reporting in SAS:

• Introduction to predictive modeling: decision trees, clustering, and classification.

• How to implement machine learning algorithms in SAS.

• Model evaluation: accuracy, precision, recall, ROC curves, and confusion matrices.

• Techniques for generating automated reports and dashboards using SAS.

• The process of integrating SAS outputs with other tools: Excel and Power BI for comprehensive reporting.