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Statistical Thinking for Industrial Problem Solving, presented by JMP

SAS via Coursera


Statistical Thinking for Industrial Problem Solving is an applied statistics course for scientists and engineers offered by JMP, a division of SAS. By completing this course, students will understand the importance of statistical thinking, and will be able to use data and basic statistical methods to solve many real-world problems. Students completing this course will be able to: • Explain the importance of statistical thinking in solving problems • Describe the importance of data, and the steps needed to compile and prepare data for analysis • Compare core methods for summarizing, exploring and analyzing data, and describe when to apply these methods • Recognize the importance of statistically designed experiments in understanding cause and effect


  • Course Overview
    • In this module you learn about the course and about accessing JMP software in this course.
  • Module 1: Statistical Thinking and Problem Solving
    • 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.
  • Module 2A: Exploratory Data Analysis, Part 1
    • 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.
  • Module 2B: Exploratory Data Analysis, Part 2
    • 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.
  • Module 3: Quality Methods
    • 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.
  • Module 4: Decision Making with Data
    • 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.
  • Module 5: Correlation and Regression
    • 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.
  • Module 6: Design of Experiments (DOE)
    • 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.
  • Module 7: Predictive Modeling and Text Mining
    • Learn how to identify possible relationships, build predictive models and derive value from free-form text.
  • Review Questions and Case Studies
    • In this module you have an opportunity to test your understanding of what you have learned.

Taught by

Mia Stephens and Ledi Trutna


4.8 rating at Coursera based on 81 ratings

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