Statistical Thinking for Industrial Problem Solving

JMP Certified Associate: Statistical Thinking for Industrial Problem Solving

During this performance-based examination, candidates will apply the skills and knowledge necessary to apply statistical thinking and fundamental statistical methods to solve industrial problems.

Successful candidates should have the ability to:

  • Explain the importance of using data and applying statistical methods to solve problems.
  • Develop a clearly defined problem statement to translate a business problem into an analytics problem
  • Compile and prepare data for analysis.
  • Visually and interactively explore data to identify potential root causes of variation.
  • Translate analytics results into an actionable decisions and results.
  • Interpret the components of a control chart to determine if a process is stable.
  • Design a measurement system analysis (MSA) and interpret the results of an MSA.
  • Conduct a capability analysis and interpret Cp, Cpk, Pp and Ppk.
  • Interpret statistical intervals and hypothesis test results.
  • Describe the impact of sampling and sample size on statistical decision making.
  • Explain the difference between correlation and causation.
  • Fit a simple regression model and interpret the results.
  • Interpret analysis results for multiple linear regression and binary logistic regression.
  • Explain the importance of DOE in developing knowledge of cause and effect.

Exam Content & Pricing

Candidates who earn this credential will have earned a passing score on the Statistical Thinking for Industrial Problem Solving exam.

  • 55-60 multiple-choice questions
  • Passing score is 725; uses a score range from 200 to 1,000 points. For more information about scaled scores see our FAQ.
  • 150 minutes to complete exam.
  • Use exam ID A00-910; required when registering with Pearson VUE.
  • Candidates will use JMP 15 software to perform this exam

Exam fee: $180 USD in the U.S. and most other countries. For country-specific pricing, click here.

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