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JMP® Workshops

Workshops designed to equip you with the tools that you need to learn the basics, covering four main areas: Data Management, Basics Statistics, Quality Methods and Design of Experiments.

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Workshop 1: Data Management

 

Working with Data

In any analysis problem, most of your time is spent preparing the data for analysis. JMP makes this process easy with powerful data manipulation capabilities. Learn how to manage data to meet any analysis challenge.

 

Open Files (File> Open)

Import Multiples (File> Import Multiple)

Join Tables (Tables> Concatenate)

Transpose Data (Tables> Transpose)

Recode Data (Cols> Recode)

Create Formulas (Cols> Formula)

Save Reports (File> Save As)

Journal Files (File> Save As)

Publish Reports (File> Publish)

Create Dashboards (Window> Combine)

Project Files (Window> Move to)

 

Graph Builder

JMP produces specialized graphs and plots with supporting tables, statistics and maps. The drag-and-drop interface is intuitive, lets you get started quickly and gives you a ribbon of options to use to select alternate graph types. Advanced features allow you to control format and appearance to create persuasive visualizations.

 

Hybrid Fuel Economy (Help> Sample Data Library)

Row Markers (Row> Markers)

Graph Builder Dialogue (Graph> Graph Builder)

Graph Types (Graph Builder> Continuous v Categorical)

Jitter Limit (Graph Builder> Points)

Summary Statistic (Bar> Mean)

Format Labels (Select Label > Right Click Font)

Format Axis (Select Axis> Double Click)

Copy Graph (Tools> Selection)

 

Workshop 2: Basic Statistics

 

Statistical Methods

Today, statistics has become one of the fastest growing subjects and become a real differentiator and value proposition for business. We use this pharmaceutical scenario to outline some common statistical methods that are used in health and life sciences.

 

Scenario: A pharmaceutical company is developing a new drug to treat the effects of cholesterol. We have a group of patients; some are on a treatment for cholesterol and others are in the control group. The research manager collects patient data and wants to understand whether there are any significant relationships between variables.

 

Distributions

Hypothesis Testing

Linear Regression

Multivariate Regression

Logistic Regression

Sampling Plans

 

Visual Analytics

JMP is the perfect tool for exploring data. Whether you are searching for relationships, patterns or outliers, learn how to look at your data to gain critical insights through:

 

Text Explorer

Geographical Maps

Table Enhancements

Bubble Plot

Publish and Share Reports

 

Workshop 3: Quality Methods

 

Process Validation

Process validation is defined as establishing by objective evidence that a process consistently produces a result or product meeting predetermined requirements.

 

Scenario: A multinational production facility is managed by the engineering manager. To ensure that production is stable and in control the manager collects process data for three critical to quality parameters time, temperature and pressure and...

 

  1. wants to understand the distribution of the data
  2. decide whether the critical parameters are on target,
  3. determine whether the process is stable and in control,
  4. demonstrate the parameters are within specification, and
  5. design an experiment to model input and output variables 

and optimize process settings to maximize the seal strength.

 

Reliability Engineering

Reliability is defined as the probability that a product adequately performs its function for a specified period/cycles under specified conditions.

 

Scenario: A multinational oil and gas production facility is managed by the site engineering manager. Two new pump designs are being assessed for their reliability over time. The site engineering manager conducts life test and collects the time and failure data and then wants to...

 

  1. understand the distribution of the life data
  2. compare the distributions of the two designs,
  3. estimate product reliability at the warranty period,
  4. determine which design has the lowest probability of failure
  5. determine an appropriate sample size to conduct a follow up study.

 

Process Improvement

Quality Control is about methods, measuring and managing. It’s about uncovering problems and finding solutions using the right techniques at the right time to make things better.

 

Kaoru Ishikawa taught quality systems in Japan and promoted quality management. He believed that most problems could be solved directly by using the seven basic quality tools.

 

  1. Histogram
  2. Check Sheet
  3. Pareto Chart
  4. Cause and Effect
  5. Stratification
  6. Scatter Plot
  7. Control Charts

 

Workshop 4: Design of Experiments

 

Custom Design

Use the Custom Design platform to construct optimal designs that are custom built for your specific experimental setting. The Custom Design platform constructs a wide variety of design types, including: Screening, Response Surface, Mixture, Random Block and Split Plots.

 

Your employer is a local mid-size coffee roaster. You need to address the strength of individually brewed twelve-ounce cups of coffee. Your goal is to determine which factors have an effect on coffee strength and to find optimal settings for those factors.

 

Full Factorial Design

A full factorial design defines an experiment where trials are run at all possible combinations of factor settings. A full factorial design allows the estimation of all possible interactions.

 

In a full factorial design, you perform an experimental run at every combination of the factor levels. The sample size is the product of the numbers of levels of the factors. In this example, construct a full factorial design to study the effects of five two-level factors (Feed Rate, Catalyst, Stir Rate, Temperature and Concentration) on the yield of a reactor. Because there are five factors, each at two levels, the full factorial design includes at least 2^5 Factorial = 2x2x2x2x2 = 32 runs.

 

Mixture Design

Use the Mixture Design platform to build experiments with factors that are components in a mixture. In mixture experiments, a factor’s value is its proportion of the mixture, which falls between zero and one. Mixture experiments have three or more factors with the sum of the factor proportions equal to one (100%). Mixture experiments differ from other experimental types in that you cannot vary factors independently of one another. When you change the proportion of one factor, the proportion of one or more other factors must also change.

 

A ternary plot is a two-dimensional representation of three mixture components that sum to a constant. The plot is an equilateral triangle with an edge for each component. Simplex Centroid Design...

 

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