SAS Advanced Statistical Modeling Training Course

Course Introduction

The SAS Advanced Statistical Modeling Training Course is designed to equip participants with comprehensive knowledge and practical skills in advanced statistical modeling, predictive analytics, multivariate analysis, and data-driven decision-making using Statistical Analysis System (SAS) software. In today's data-intensive environment, governments, research institutions, healthcare organizations, financial institutions, development agencies, and private enterprises increasingly depend on advanced statistical models to analyze complex datasets, identify relationships among variables, predict future outcomes, and support strategic planning and policy formulation. This course provides participants with practical competencies in advanced SAS procedures, statistical modeling techniques, predictive analytics, machine learning concepts, and professional reporting methodologies necessary for high-quality research and organizational performance improvement.

The course focuses on advanced principles and practical applications of SAS statistical modeling, including data preparation and management, regression modeling, generalized linear models, multivariate analysis, time series forecasting, survival analysis, mixed models, predictive analytics, model diagnostics, and advanced reporting techniques. Participants will gain practical experience in building robust statistical models, validating model assumptions, interpreting complex analytical outputs, and generating evidence-based recommendations that support organizational planning and decision-making. The course emphasizes practical applications of SAS statistical modeling in economics, public health, social sciences, agriculture, finance, business intelligence, monitoring and evaluation, and development programming.

As organizations increasingly adopt digital transformation initiatives, predictive analytics technologies, and evidence-based management systems, competencies in advanced statistical modeling have become indispensable for researchers, statisticians, epidemiologists, economists, data scientists, monitoring and evaluation specialists, policy analysts, and organizational leaders. This training emphasizes analytical reasoning, computational thinking, statistical rigor, and quantitative problem-solving approaches that improve research quality, strengthen analytical capabilities, and facilitate informed and strategic decision-making.

Through presentations, practical exercises, computer-based applications, collaborative group work, programming assignments, and real-world case studies, participants will develop competencies necessary to manage complex datasets, develop sophisticated analytical models, interpret findings accurately, and communicate analytical results effectively. Upon completion of this course, participants will be capable of applying advanced SAS statistical modeling techniques to solve complex organizational challenges, improve forecasting capabilities, strengthen evidence systems, and contribute to innovation and evidence-based management practices.

Course Objectives

Upon completion of this course, participants will be able to:

1.     Understand advanced principles and applications of statistical modeling using SAS.

2.     Prepare and manage complex datasets for advanced analytical procedures.

3.     Develop and interpret regression and multivariate statistical models.

4.     Apply predictive analytics and forecasting techniques using SAS.

5.     Conduct generalized linear modeling and mixed-effects analyses.

6.     Perform survival analysis and time-to-event modeling procedures.

7.     Evaluate model assumptions and perform diagnostic assessments.

8.     Generate professional analytical reports and data visualizations.

9.     Develop evidence-based recommendations from statistical findings.

10.  Apply advanced statistical modeling techniques to support strategic planning and organizational decision-making.

Organizational Benefits

Organizations that invest in this training will benefit by:

1.     Strengthening evidence-based planning and strategic decision-making capabilities.

2.     Improving predictive analytics and forecasting systems.

3.     Enhancing research quality and analytical rigor.

4.     Building staff competencies in advanced statistical modeling techniques.

5.     Strengthening monitoring, evaluation, and reporting systems.

6.     Improving organizational performance measurement and impact assessment.

7.     Supporting effective policy development and resource allocation.

8.     Enhancing business intelligence and knowledge management systems.

9.     Strengthening innovation and digital transformation initiatives.

10.  Promoting accountability, operational excellence, and continuous improvement.

Target Participants

This course is designed for statisticians, researchers, epidemiologists, economists, data analysts, data scientists, monitoring and evaluation specialists, public health professionals, policy analysts, business analysts, financial analysts, consultants, government officials, academicians, postgraduate students, development practitioners, market researchers, project managers, and professionals involved in advanced statistical analysis, predictive modeling, research, and evidence-based decision-making.

Course Outline

Module 1: Foundations of Advanced Statistical Modeling Using SAS

1.     Principles and applications of advanced statistical modeling

2.     Overview of SAS analytical environments and procedures

3.     Statistical modeling workflows and methodologies

4.     Model selection principles and analytical frameworks

5.     Best practices in advanced statistical analysis

6.     General Case Study: Developing analytical frameworks for organizational performance assessment

Module 2: Advanced Data Management and Preparation

1.     Data importation and integration procedures

2.     Data cleaning and preprocessing techniques

3.     Managing missing values and outlier detection

4.     Data transformation and feature engineering methods

5.     Preparing datasets for advanced statistical analysis

6.     General Case Study: Preparing public health datasets for predictive modeling applications

Module 3: Regression Analysis and Predictive Modeling

1.     Principles of regression analysis techniques

2.     Simple and multiple linear regression models

3.     Logistic regression and classification techniques

4.     Model diagnostics and assumption testing

5.     Interpretation and reporting of regression outputs

6.     General Case Study: Identifying determinants of organizational productivity and performance

Module 4: Generalized Linear Models

1.     Principles of generalized linear modeling

2.     Binary and multinomial response modeling techniques

3.     Count data analysis and Poisson regression procedures

4.     Model estimation and validation techniques

5.     Interpretation of generalized linear model outputs

6.     General Case Study: Modeling determinants of healthcare service utilization patterns

Module 5: Multivariate Statistical Analysis

1.     Principles of multivariate analytical methods

2.     Factor analysis and dimension reduction procedures

3.     Principal component analysis techniques

4.     Discriminant analysis and classification methods

5.     Interpretation of multivariate analytical findings

6.     General Case Study: Segmenting consumers based on purchasing behavior patterns

Module 6: Mixed Models and Hierarchical Analysis

1.     Principles of mixed-effects modeling techniques

2.     Random effects and fixed effects estimation methods

3.     Hierarchical and nested data structures

4.     Model specification and validation procedures

5.     Interpretation and reporting of mixed model outputs

6.     General Case Study: Assessing educational outcomes across schools and regions

Module 7: Survival Analysis Techniques

1.     Principles of survival and time-to-event analysis

2.     Kaplan-Meier estimation techniques

3.     Cox proportional hazards modeling procedures

4.     Model diagnostics and assumption testing

5.     Interpretation of survival analytical outputs

6.     General Case Study: Analyzing patient survival outcomes and treatment effectiveness

Module 8: Time Series Analysis and Forecasting

1.     Principles of time series analytics

2.     Trend and seasonality decomposition techniques

3.     Forecasting models and predictive methods

4.     Model validation and forecasting accuracy measures

5.     Applications in planning and resource management

6.     General Case Study: Forecasting healthcare demand and resource requirements

Module 9: Advanced Predictive Analytics

1.     Principles of predictive analytics and machine learning

2.     Model development and training procedures

3.     Performance evaluation and validation techniques

4.     Risk analysis and classification methods

5.     Applications in decision support systems

6.     General Case Study: Developing predictive models for customer retention and service demand

Module 10: Model Diagnostics and Validation

1.     Principles of model assessment and validation

2.     Goodness-of-fit and performance evaluation techniques

3.     Residual analysis and assumption testing procedures

4.     Cross-validation and sensitivity analysis methods

5.     Model refinement and optimization strategies

6.     General Case Study: Evaluating predictive models for policy impact assessment

Module 11: Data Visualization and Analytical Reporting

1.     Principles of analytical reporting and communication

2.     Development of tables and graphical presentations

3.     Dashboard design and visualization techniques

4.     Interpretation and presentation of analytical findings

5.     Preparation of evidence-based reports and recommendations

6.     General Case Study: Developing organizational performance dashboards and analytical reports

Module 12: Emerging Trends in Statistical Modeling and Analytics

1.     Integration of SAS with big data technologies

2.     Machine learning and artificial intelligence applications

3.     Advanced analytics and business intelligence systems

4.     Cloud computing and collaborative analytical environments

5.     Future trends in statistical modeling and predictive analytics

6.     General Case Study: Designing integrated analytical systems for organizational transformation and strategic planning

General Information

1.     Customized Training: All our courses can be tailored to meet the specific needs of participants.

2.     Language Proficiency: Participants should have a good command of the English language.

3.     Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.

4.     Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).

5.     Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.

6.     Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.

7.     Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.

8.     Additional Services: Accommodation, pickup services, freight booking, and visa processing arrangements are available upon request at discounted rates.

9.     Equipment: Tablets and laptops can be provided to participants at an additional cost.

10.  Post-Training Support: We offer one year of free consultation and coaching after the course.

11.  Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.

12.  Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.

13.  Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.

14.  Website: Visit our website at www.fdc-k.org for more information.