Statistical Analysis in pharmaceutical industry

Statistics in Pharmaceutical Science: Principles and Applications
Master Statistical Tools to Strengthen Decision-Making, Quality, and Process Performance

This training is designed for pharmaceutical professionals with minimal or no previous exposure to statistics. It provides a strong understanding of statistical concepts and equips participants to apply statistical methods confidently in pharmaceutical operations.
The program teaches how to plan experiments, analyze data, troubleshoot process variations, interpret trends, categorize out-of-control results, and evaluate product and process capability. The course emphasizes real-world applicability, enabling participants to use statistical tools to improve product quality, process consistency, and regulatory reporting.

Who Should Participate

Management

Research and Development

Quality Assurance, Validation and Process Engineers

Quality Control

Regulatory

Production Management and Supervisors

Clinical Research

Course Objectives

After completing this course, participants will be able to:

Use statistics to accurately plan experiments, generate and analyze data

Apply statistics to specify accuracy and variation of a process

Use control charts to troubleshoot, categorize out-of-control processes, and assess performance

Evaluate process capability using Cp, Cpk and other performance indices

Assess variability and special causes of variation

Identify statistical strategies for quality improvement

Review product and process quality using statistical tools for Annual Quality Review (AQR)

Use advanced statistical methodologies such as Correlation, Regression and DOE to improve scientific, commercial and business decisions

Course Outline

Module 1 – Statistical Concepts to Understanding Statistical Analysis

A. Statistical Concepts and Terminology

B. Variable, Variation, Variance

C. Frequency Distributions and Normal Distribution

D. Descriptive Statistics and Graphs

E. Statistical Parameters and Sample vs Population

F. Statistical Inference and Hypothesis Testing

G. t-test, F-test & ANOVA

H. Analysis of Variance and Two-Way ANOVA

I. Sampling and Sampling Errors

Module 2 – Statistical Process Control (SPC)

A. SPC Concepts and Terminology

B. Quality Management and SPC

C. Control Charts – Variable, Attribute & Nonconforming Data

D. Process Capability & Performance Indices (Cp, Cpk)

E. Use of Control Charts and Process Capability Data

F. Pareto Analysis, Scatter Diagrams, Cause-and-Effect Diagrams

G. Implementation of SPC in Manufacturing

Module 3 – Design of Experiments (DOE)

A. Why DOE is used

B. Scientific Approach to Experimentation

C. Correlation and Regression

D. Factorial Design of Experiments

E. Screening Designs, Response Surface Methodology, Mixture Designs

F. DOE for formulation and process optimization

Duration

Three days

Key Takeaways

Ability to apply statistical tools to real pharmaceutical operations

Better understanding of product and process behavior using data

Improved troubleshooting and root-cause analysis using statistical models

Ability to design, monitor and optimize experiments using DOE

Stronger scientific documentation for quality submissions and AQR reporting

Enhanced decision-making based on reliable statistical evidence

Speak to a Training Specialist

Contact

Building #10, Ground Floor, Business Park, Amman, Jordan
+962 (79) 1533337
m.abuzour@agonme.com

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Services

Management Representative

Training Programs

Statistical Analysis in pharmaceutical industry