Training & Education: Curriculum

This 1 day class is the launching point for doing analysis with JMP®. It will cover the basics of how to use the JMP® statistical software: creating and editing tables, creating descriptive statistics, working with report windows, and creating graphics.

Who Should Attend
Engineers, scientists, and technicians who need a faster way to interact and visualize data to respond to problems or find insights that lead to higher quality and productivity.

Duration: 1 day

Prerequisites
Computer knowledge.

Expected Results
After completing this course, participants will be able to:

• exploit JMP®’s data table features and design
• create, edit and tables in JMP®
• get help using JMP® in a variety of ways
• import data from Excel®, text files and databases
• correcting erroneous data
• format columns and change other column properties
• representing multiple variant sets of data in one data table
• manipulating and joining data tables
• several interactive ways to select and mark observations
• interactively create summaries and graphs using Tabulate and Graph Builder
• creating a variety of charts including bubble plots
• creating journals
• exporting reports and report elements to word processing and presentation applications

All analysis comes down to two things: making comparisons and finding relationships. These essential building blocks are thoroughly applied with industrial examples in this course.

This 2 day class will cover descriptive statistics, graphical analysis, probability models, confidence intervals, and hypothesis testing.

Who Should Attend
Engineers, scientists, and technicians who need to learn how to summarize data and use statistical methods to make comparisons and look for relationships among single variables.

Duration: 2 days

Prerequisites
JMP Tables and Charts or equivalent software knowledge; knowledge of algebra.

Expected Results
After completing this course, participants will be able to:

• characterize where processes are located and how they vary
• determine the appropriate statistics to adequately describe a distribution of data
• determine the appropriate graph to adequately display data
• describe the relationship between two or more factors or responses
• characterizing output using probability models
• manage risk through data driven decision making
• universally interpret and apply hypothesis testing
• assessing compliance to targets in the presence of uncertainty
• effectively comparing methods, materials, conditions in the presence of uncertainty
• understand and communicate variability
• respond to conflicting objectives among analytical power, variation, sample size and differences to detect
• balancing making too many false alarms with missing too many opportunities in analysis

Beyond single-factor analysis there are opportunities to look for more complex, but readily identifiable relationships among multiple factors, comprehensively. This sort of analysis usually provides more opportunities for improvement than single and two-factor analysis.

This 2 day class will cover multiple comparison tests and statistical model building. Analysis of Variance concepts and variance components analysis will be covered. The examples and exercises completed in this class use the JMP® software package.

Who Should Attend
Engineers, scientists, and technicians who need to learn how to build models and test and examine multiple comparisons and multi-factor relationships.

Duration: 2 days

Prerequisites
Single-factor Insights & Solutions from Data or equivalent software and statistical knowledge.

Expected Results
After completing this course, participants will be able to:

• build multi-variate models and detect and remedy commonly occurring traps involved in modelling
• universally evaluate model significance and suitability
• model using linear and nonlinear models
• take advantage of fixed effect, random effect and mixed models
• model both crossed and nested factors, commonly found in industry
• understand the theory of Analysis of Variance tables and be able to interpret standard ANOVA tables.
• build a model of a process from gathered data and assess the goodness-of-fit of the model.

Design of Experiment (DOE or DOX) is perhaps the most effective and efficient way to do research today. Engineers and scientists who exploit design of experiments will be able to advance their organizations competitiveness by seeing deeper insights in less time and cost.

Most DOEs have one or more of the following objectives

• determine which factor is causing problems and fix it
• determine the width of the process window, especially in relation to control limits and specification limits so that the process can stay away from the cliff (Sensitivity Testing)
• determine which path to take in development
• figure out how to reduce variation
• try proposed ideas and see if they lead to improvement
• test lower cost factor settings
• finding ways to compensate changes in one condition or material without messing everything else up.

Design of Experiments 1

This course begins the process in understanding and applying design of experiments. The methods in this 2 day course alone will advance research and improvement productivity considerably over informal methods.

Who Should Attend
Engineers, scientists, and technicians who are involved in characterizing, evaluating, and improving processes and equipment.

Duration: 2 days

Prerequisites
Multi-factor Insights & Solutions from Data or equivalent software and statistical knowledge.

Expected Results
After completing this course, participants will be able to:

• design experiments to maximize efficiency and analytical power while being practical
• develop a strategy for experimentation
• apply the scientific method in the form of Plan-Do-Study-Act
• design, analyze and interpret screening experiments

Design of Experiments 2

This course carries forward from the Design of Experiments 1 to focus on some very effective, advanced DOE techniques. Some of these advanced methods have been made widely available with software over the past 10 years.

Does batch processing in a manufacturing environment make designing ordinary experiments a challenge? Did you discover that your response does not increase or decrease in a straight line like all those classroom examples? Want to know what to do next? This 2 day class provides an introduction to the practice of experimentation for process optimization. Topics include response surface designs, split plot designs, strip plot designs, and computer-aided (optimal) designs. Analysis and interpretation topics integrate the use of JMP to support analysis of variance and multiple linear regression.

Who Should Attend
Engineers, scientists, and technicians who are involved in characterizing, evaluating, and improving processes and equipment.

Duration: 2 days

Prerequisites
Design of Experiments 1 or equivalent software and statistical knowledge.

Expected Results
After completing this course, participants will be able to:

• implement the Path of Steepest Ascent method of designing experiments
• design, analyze, and interpret response surface methodology experiments
• understand the complexity of doing experimental design in a batch processing environment
• understand the costs and benefits of performing an optimal experiment
• conduct Mixture design of experiments
• robust optimization

Just Enough DOE for Managers & Operators

DOEs do not happen in a vacuum. They require management approval and operator support. This 1 day course will show managers and operators and anyone else interested how DOEs work primarily using graphs and not without too technical and buried in statistical-ese.

Duration: 1 day

Prerequisites
Everyday computing skills and some familiarity with Excel®

Expected Results
After completing this course, participants will be able to:

• adjust their practices and policies to support effective DOEs
• participate in designing a DOE
• warn of problems before and during a DOE

Are you sure that the signals you get in your analysis are from the system of interest or could they be from faults within the measurement system itself?

Gauge Studies & Measurement Systems Analysis

This 1 day class provides an introduction to the measurement systems analysis studies. Topics covered include determining the factors to study, conducting the study, and using graphical and numerical analysis to summarize the results.

Who Should Attend
Engineers, scientists, and technicians who are involved in evaluating metrology tools.

Duration: 1 day

Prerequisites
Multi-factor Insights & Solutions from Data or equivalent software and statistical knowledge.

Expected Results
After completing this course, participants will be able to:

• design and analyze a gauge study
• evaluate linearity and bias
• identify and correct problem areas involved in measurement
• be able to determine whether changes to the measurement system lead to improvement

Advanced Metrology Setup and Control

This 8 hour class provides an introduction to the measurement systems analysis studies. Topics covered include determining the factors to study, conducting the study, and using graphical and numerical analysis to summarize the results.

Gauge Studies & Measurement Systems Analysis
Engineers, scientists, and technicians who are involved in evaluating metrology tools but with more challenging metrology situations.

Who Should Attend
Engineers, scientists, and technicians who are involved in evaluating metrology tools.

Duration: 1 day

Prerequisites
Gauge Studies & Measurement Systems Analysis Some basic knowledge of design of experiments would be helpful, though is not strictly necessary.

Expected Results
After completing this course, participants will be able to:

• conduct an MSA with fixed and random factors
• conduct an MSA with crossed and nested data arrangements

This 1 day class provides an introduction to statistical process control (SPC). Topics covered include historical SPC, passive data collection, sampling plan determination, control limit calculation for continuous and batch processing, and out of control action plans.

Who Should Attend
Engineers, scientists, and technicians who are involved in process control.

Duration: 1 day

Prerequisites
Multi-factor Insights & Solutions from Data or equivalent software and statistical knowledge.

Expected Results
After completing this course, participants will be able to:

• perform passive data collection and determine significant sources of variability
• determine the correct set of control charts for their process
• calculate the correct control limits on the process
• determine whether a change in process conditions has lead to an improvement in process behavior
• know when to fix control limits
• how to rationally subgroup
• conduct SPC for variable and attribute data

Interested in writing your own scripts or programs in JMP®? Want to do things your own way?

JSL can be quite challenging – it’s not for everyone. But just about every JMP® user is looking for ways to be more productive. There are plenty of opportunities, even among the basics, for JMP® users to make their lives easier with JSL. This class will get you more productive!

Who Should Attend
Anyone interested in learning to program in JMP’s scripting language.

Duration: 1 day

Prerequisites
JMP® Tables & Charts or equivalent

Expected Results
After completing this course, participants will be able to:

• save scripts from graphs and summary tables
• tie together multiple scripts
• select and re-arrange specific items from reports
• write loops
• write scripts that ask for user input!

JSL is not the easiest language to learn because it’s more than a language. Programming in JSL means playing with the bits and pieces that actually make up JMP® and since JMP® was not designed to be a programming language this can lead to some challenges.

This 3-day class provides an introduction to JMP Scripting Language (JSL). Topics covered will be messaging, tables, matrices, functions, graphics, platforms, communication with the user, and accessing databases.

Who Should Attend
Engineers, scientists, and technicians who are JMP “power users,” have a programming aptitude, and wish to create substantial applications for others.

Duration: 3 days

Prerequisites
Significant JMP experience.

Expected Results
After completing this course, participants will be able to:

• learn how to learn how JMP® behaves
• write, debug, and run JMP scripts
• learn two ways of making scripts abstract and general so that they are self-maintaining and widely applicable

Want to reduce the variation in your manufacturing process? What are your biggest sources of variation? Where would your efforts be most effective if you were to reduce some of the variation?

Reducing variation is its own reward because subsequent improvements will be much easier to find. We find that most engineers and analysts working in manufacturing spend the bulk of their time responding to situations. That’s understandable but a lot of opportunity is not even considered. We recommend that every manufacturer form a group focused to a large extend on reducing variation. This will lead to insights that otherwise would not be found that will, in turn, lead to fewer situations to respond to.

Who Should Attend
Engineers in a manufacturing environment working to reduce variation.

Duration: 2 days

Prerequisites
Multi-factor Insights & Solutions from Data or equivalent.

Expected Results
After completing this course, participants will be able to:

• understand and communicate variance components
• use variance components to decide what the minimum necessary control charts
• create and understand appropriate control charts in a batch processing environment.

This 3-day class provides an introduction to the basics of how to use the JMP® statistical software, engineering and scientific approaches to descriptive statistics, graphical analysis, hypothesis testing, analysis of variance, model building and statistical process control.

This intensive 3-day course consolidates the material and deepens the experience offered in the following courses:

• JMP® Tables & Charts
• Single & Two-factor Insights & Solutions from Data
• Multi-factor Insights & Solutions from Data

The advantage of taking this courses in one, 3-day stretch is higher retention, better understanding in how the methods connect to one-another and more opportunity to exploit statistical methods immediately following the course.

Who Should Attend
Engineers, scientists, and technicians who intend to do product or process improvement.

Duration: 3 days

Prerequisites
No prerequisite knowledge, though general familiarity with computers and spreadsheet software is helpful.

Expected Results
After completing this course, participants will be able to:

• exploit JMP®’s data table features and design
• create, edit and tables in JMP®
• get help using JMP® in a variety of ways
• import data from Excel®, text files and databases
• correcting erroneous data
• format columns and change other column properties
• representing multiple variant sets of data in one data table
• manipulating and joining data tables
• several interactive ways to select and mark observations
• interactively create summaries and graphs using Tabulate and Graph Builder
• creating a variety of charts including bubble plots
• creating journals
• exporting reports and report elements to word processing and presentation applications
• characterize where processes are located and how they vary
• determine the appropriate statistics to adequately describe a distribution of data
• determine the appropriate graph to adequately display data
• describe the relationship between two or more factors or responses
• characterizing output using probability models
• manage risk through data driven decision making
• universally interpret and apply hypothesis testing
• assessing compliance to targets in the presence of uncertainty
• effectively comparing methods, materials, conditions in the presence of uncertainty
• understand and communicate variability
• respond to conflicting objectives among analytical power, variation, sample size and differences to detect
• balancing making too many false alarms with missing too many opportunities in analysis
• build multi-variate models and detect and remedy commonly occurring traps involved in modelling
• universally evaluate model significance and suitability
• model using linear and nonlinear models
• take advantage of fixed effect, random effect and mixed models
• model both crossed and nested factors, commonly found in industry
• understand the theory of Analysis of Variance tables and be able to interpret standard ANOVA tables
• build a model of a process from gathered data and assess the goodness-of-fit of the model