Statistical Thinking for Industrial Problem Solving, presented by JMP

Go to Course: https://www.coursera.org/learn/statistical-thinking-applied-statistics

Introduction

**Course Review: Statistical Thinking for Industrial Problem Solving (by JMP)** In the contemporary landscape of data-driven decision-making, understanding how to utilize statistical methods and approaches is paramount for professionals in science and engineering. The course "Statistical Thinking for Industrial Problem Solving," presented by JMP, a division of SAS, tackles this need with a comprehensive understanding of applied statistics. ### Overview This course equips participants with the necessary skills to harness the power of data to solve real-world problems effectively. The curriculum is designed with an emphasis on statistical thinking, helping students appreciate its significance while learning practical methodologies to analyze and interpret data. By the end of the course, participants will not only grasp the importance of data but will also be proficient in various statistical techniques that are foundational in industry settings. ### Course Structure & Syllabus The course consists of several well-defined modules that lay a solid groundwork in statistical thinking: 1. **Course Overview**: - Initial insights about the course and information on accessing JMP software. 2. **Module 1: Statistical Thinking and Problem Solving**: - Students explore process variation, learn to create process maps, and utilize problem-solving tools. 3. **Module 2A & 2B: Exploratory Data Analysis**: - Covering the basics and advanced techniques of data visualization and statistical summaries, these modules focus on effectively communicating data insights. 4. **Module 3: Quality Methods**: - This module emphasizes tools for quality management, encompassing control charts and measurement systems analysis, vital for maintaining product and process excellence. 5. **Module 4: Decision Making with Data**: - Participants delve into statistical inference, exploring hypothesis testing, statistical intervals, and the essential relationship between sample size and study power. 6. **Module 5: Correlation and Regression**: - This section teaches the analysis of relationships between variables through scatterplots and regression models, fundamental skills for predictive analytics. 7. **Module 6: Design of Experiments (DOE)**: - An introduction to DOE prepares students for designing effective experiments, a critical element in industrial problem solving. 8. **Module 7: Predictive Modeling and Text Mining**: - Participants learn about building predictive models and extracting valuable insights from textual data, enhancing their analytical capabilities. 9. **Review Questions and Case Studies**: - The course culminates with a chance to test knowledge through review questions and practical case studies, reinforcing learning outcomes. ### Recommendations **Who Should Enroll**: This course is ideal for scientists, engineers, and professionals involved in data analysis and decision-making within industrial environments. Those looking to enhance their analytical skills with a statistical approach will find this course immensely beneficial. **Why Take This Course**: - **Comprehensive Approach**: The course not only covers theoretical aspects but also emphasizes their practical applications in the industry. - **User-Friendly Software**: By utilizing JMP software, students gain hands-on experience that will serve them well in their professional endeavors. - **Expert Instruction**: Being presented by JMP—a well-respected name in the analytics world—ensures that learners are receiving quality content grounded in real-world application. **Conclusion**: "Statistical Thinking for Industrial Problem Solving" stands out as a valuable resource for those eager to understand and apply statistical methods in industrial contexts. The combination of theoretical principles and applied learning, along with the opportunity to work with JMP software, makes this course a highly recommended choice for anyone serious about enhancing their statistical competency. Take the leap into the world of statistical analysis and unlock the potential of data-driven decision-making within your organization!

Syllabus

Course Overview

In this module you learn about the course and about accessing JMP software in this course.

Statistical thinking is about understanding, controlling and reducing process variation. Learn about process maps, problem-solving tools for defining and scoping your project, and understanding the data you need to solve your problem.

Learn the basics of how to describe data with basic graphics and statistical summaries, and how to explore your data using more advanced visualizations. You’ll also learn some core concepts in probability, which form the foundation of many methods you learn throughout this course.

Learn how to use interactive visualizations to effectively communicate the story in your data. You'll also learn how to save and share your results, and how to prepare your data for analysis.

Learn about tools for quantifying, controlling and reducing variation in your product, service or process. Topics include control charts, process capability and measurement systems analysis.

Learn about tools used for drawing inferences from data. In this module you learn about statistical intervals and hypothesis tests. You also learn how to calculate sample size and see the relationship between sample size and power.

Learn how to use scatterplots and correlation to study the linear association between pairs of variables. Then, learn how to fit, evaluate and interpret linear and logistic regression models.

In this introduction to statistically designed experiments (DOE), you learn the language of DOE, and see how to design, conduct and analyze an experiment in JMP.

Learn how to identify possible relationships, build predictive models and derive value from free-form text.

In this module you have an opportunity to test your understanding of what you have learned.