Tabtrainer Minitab: One Sample Test Of Proportion

Achieve top-level expertise in Minitab with Prof. Dr. Murat Mola, recognized as Germany's Professor of the Year 2023.

What you'll learn

Conduct one-sample proportion tests to evaluate defect rates in production.

Calculate sample sizes using hypothesis power analysis for binomial data.

Apply binomial distribution to quality assurance and production analysis.

Differentiate between discrete and continuous data in statistical contexts.

Interpret p-values and power curves to validate hypothesis test outcomes.

Recommend data-driven quality improvement measures based on analysis.

Requirements

No Specific Prior Knowledge Needed: The course is suitable for beginners, as all topics are explained in a practical and step-by-step manner.

Description

Course Description:This in-depth course is designed to provide professionals with the tools and knowledge to excel in statistical quality control and production optimization. Focusing on practical application, participants will delve into hypothesis testing, power analysis, and the use of binomial distribution to analyze and manage defect rates effectively. This course bridges the gap between theoretical statistical concepts and real-world production challenges, equipping participants with actionable skills to drive data-based decisions and improve production processes.Throughout the program, participants will engage with real-world scenarios, hands-on exercises, and detailed case studies from the manufacturing sector. These activities ensure that learners not only understand statistical methods but also can apply them confidently to improve quality assurance practices. The course emphasizes tools and techniques used to identify critical quality issues, implement corrective actions, and maintain efficiency while meeting customer requirements.This course is ideal for quality assurance professionals, production managers, engineers, Six Sigma practitioners, and decision-makers in manufacturing industries who seek to enhance their analytical and decision-making capabilities. No advanced statistical background is required, making the course accessible to those eager to master practical statistical applications.Learning Objectives:By the end of this course, participants will:Master hypothesis testing techniques to evaluate defect rates and improve quality assurance.Determine accurate sample sizes for statistical tests using power analysis tailored to production needs.Apply binomial distribution principles to analyze discrete data in production scenarios.Differentiate between discrete and continuous data to select and use the appropriate statistical methods.Interpret p-values, confidence intervals, and power curves for hypothesis validation and decision-making.Detect and evaluate changes in defect rates with a high degree of statistical confidence.Design and implement data-driven recommendations for process improvements and defect reduction.Understand the cost-benefit implications of defect rates on production profitability.Develop advanced skills in statistical tools, bridging theory and practical application in a manufacturing context.This course ensures participants leave with the ability to implement robust quality control measures, reduce production costs, and enhance process efficiency, making them invaluable assets to their organizations.

Overview

Section 1: One-Sample Test of Proportion

Lecture 1 Introduction and Business Case for One-Sample test of Proportion

Lecture 2 The Binomial Distribution

Lecture 3 Determine the correct sample size

Lecture 4 Tally for Discrete Variables: goodpart / badpart

Lecture 5 One-Sample Proportion Test

Lecture 6 Summary of the Most Important Findings

Quality Assurance Professionals: Those responsible for monitoring production processes and ensuring product quality will gain practical tools for defect analysis.,Production Managers: Managers overseeing manufacturing operations will benefit from learning how to identify and address quality issues effectively.,Six Sigma Practitioners: Professionals looking to enhance their expertise in statistical tools for process optimization and decision-making.,Engineers and Analysts: Individuals in manufacturing or technical roles seeking to apply statistical methods to real-world challenges in production.,Business Decision-Makers: Executives and leaders aiming to balance quality, cost, and efficiency in production through data-driven insights and strategies.