Courses & Training

Adsurgo award-winning instructors provide customized on-site training solutions, consulting, and products.

All JMP Training

Adsurgo has training available in virtually all disciplines of applied statistics and analytics. We can tailor a course of any length for on-site delivery or via web-ex. Where possible, we use customer examples and data sets to better connect material to job function.

Getting Started with JMP

Design of Experiments

Advanced JMP & JMP Pro

JMP Scripting Language (JSL)

Classic JMP

OTHER COURSES

& Training

Event	Date	Description
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Advanced Design of Experiments (1.0) Days	December 11, 2024 9:00 am	JMP Advanced DOE (1.0 days): This course builds on the DOE Using JMP material by providing more insight into the metrics of good designs such as prediction variance, power analysis, design optimality, and correlation between factors. We build on custom designs with constrained design regions/disallowed combinations and augmenting existing designs with new runs. We introduce new classes of designs useful in many practical applications: split plot designs (hard-to-change factors), space filling designs, mixture designs, and supersaturated (more factors than runs) designs. JMP Advanced DOE Objectives: Understand the impact of power, prediction variance, optimality criterion (A, D, or I) and correlation/alias structure in designs Create custom designs in the presence of a constrained factor space Augment designs with additional runs taking advantage of what you learned from those tests already conducted. Know when to use and be able to construct split-plot designs, supersaturated designs, space filling designs, and mixture designs Outline: DOE review • Classical designs • Metrics of a good design • Custom designs and optimality criterion Designs Methods • Constrained designs and disallowed combinations • Augmenting designs Advanced Designs • split-plot designs • supersaturated designs • space filling designs • mixture designs	View Outline and Register
Applied Statistics for Scientists (2.0 days)	December 12, 2024 9:00 am	Applied Statistics for Scientists (2.0 days): This course provides instruction on how to apply scientific approaches to data analysis by focusing on appropriate methods for analysis: descriptive statistics, hypothesis testing, analysis of variance and model building. Applied Statistics for Scientists Objectives: describe and analyze the distribution of data develop summary statistics generate and analyze statistical intervals and hypothesis tests to make data-driven decisions understand issues related to sampling and calculate appropriate sample sizes describe the relationship between and among two or more factors or responses Outline: 1. Descriptive Statistics • introducing summary statistics • describing a distribution of values • creating a summary table and tabulating data 2. Intervals • producing confidence intervals • producing tolerance intervals 3. Hypothesis Testing • introducing hypothesis testing • performing means (and variance) tests • performing proportion tests 4. ANOVA • defining analysis of variance and other terminology • discussing assumptions and interpretation • interpreting hypothesis statements for ANOVA • performing one-way ANOVA 5. Simple Linear Regression • producing scatterplots and performing correlation • performing simple linear regression 6. Model Building • introducing model building • performing N-way ANOVA • performing multiple linear regression • performing ANCOVA 7. Model Diagnostics • evaluating the validity of model assumptions • evaluating model fit	Sold Out
Data Visualization and Storytelling (1.0 days)	December 12, 2024 9:00 am	Data Visualization and Storytelling (1.0 days): This course will provide principals to effectively communicate your technical results professionally. The methods will allow you to interactively discover deeper relationships graphically more efficiently. We will provide the foundations for creating better graphical information to accelerate the insight discovery process and enhance the understandability of reported results. First principles and the “human as part of the system” aspects of information visualization from multiple leading sources are explored using representative univariate, multivariate, time series, geographic, and unstructured text data sets. Data Visualization and Storytelling Objectives: Understand data visualization principals Know the characteristics of data that help guide appropriate visualizations Be able to apply a disciplined process for designing good graphics and dashboards Create graphs for different data types to include univariate, multivariate, categorical, text, time series, geographic, and modeling output. Be able to tell the story of applied statistical analysis integrating the narrative with appropriate displays Introduction • Introduction, definitions, and historical perspectives • Examples of data visualizations and interactivity • Characteristics of data Designing Effective Graphics • Human side of data visualization • Principles of good graphic design • Data visualization methodology and best practices • Dashboards Creating the Right Graph • Univariate plots: distributions, histograms, and boxplots • Multivariate plots: scatterplots, contour plots, treemaps • Categorical data plots: pie/doughnut graphs, mosaic plots • Text/Unstructured data: word clouds, topic plots, word and documents clusters • Time series data: trend lines/forecasts, waterfall plots, sparklines • Geographic data: maps, custom maps Graphics from Modeling • Model adequacy checks • Factor profilers and optimization • Comparing competing models Telling the story • Data storytelling steps and principals • Best practices • Oral presentation tips	Sold Out
Demonstration of Comparability for Chemistry, Manufacturing, and Controls (CMC) in the Pharmaceutical Industry (0.5 days)	December 17, 2024 1:00 pm	Demonstration of Comparability for Chemistry, Manufacturing, and Controls (CMC) in the Pharmaceutical Industry (0.5 days): The FDA comparability guidance (1996) recognizes the need for manufacturers to improve manufacturing processes and analytical procedures without performing additional clinical studies to demonstrate product safety and efficacy. This guidance was extended in ICH Q5E (2004) to provide additional direction for comparing pre- and post-change manufacturing processes. Across the regulatory documents, there are only high-level recommendations for the design of an equivalence/comparability study and for setting acceptance criteria to assess the impact of the change. These documents do not generally contain prescriptive rules for setting acceptance criteria, study design, or statistical procedures for analysis. One notable exception is the draft guidance published by FDA (2019) concerning demonstration of analytical similarity. EMA also produced a report based on comments offered on a reflection paper concerning analytical comparability and similarity (2018). This course presents statistical approaches for demonstrating comparability consistent with these regulatory guidelines. In addition, this course provides JMP scripts for power and sample size determinations as well as appropriate constants for intervals used to calculate quality ranges: Demonstration of Comparability for CMC in the Pharmaceutical Industry: Explanation of differences between statistical methods used for demonstrating comparability and when each is appropriate. A list of considerations for selecting an appropriate method. An approach for setting comparability criteria. A method to determine reasonable sample sizes. Examples discussed in the course include: qualification of reference standards, comparison of manufacturing sites, partitioning of variation in gene and cell therapies, analytical bridging studies, tech and analytical transfer, scale-down model (SDM) qualification, and analytical similarity of biosimilars. Methods discussed in the course include: side-by-side plots, statistical equivalence tests for means (TOST), non-inferiority of standard deviations, and quality ranges (min-max intervals, tolerance intervals, 3-sigma intervals, and risk-based side-by-side intervals). Outline: Comparability Studies Comparability Basics and Definitions Comparability Tools and Statistical Designs CMC Applications Comparability Tools Considerations in Selecting an Approach Statistical Tests Quality Ranges Comparison of Methods Defining Acceptance Criteria and Power Calculations Power and Statistical Power Calculations Final Recommendations	Sold Out
Design of Experiments (DOE) using JMP (1.0 days)	April 12, 2023 9:00 am	DOE using JMP (1.0 days): This course focuses on how to establish a systematic approach to pharmaceutical development using design of experiments (DOE). All concepts are taught within the product quality system framework. Design of Experiments (DOE) using JMP: identify critical quality attributes (CQAs) that will be used as responses in your designs utilize risk management tools to identify and prioritize potential critical process parameters identify critical process parameters and develop a functional relationship between those process parameters and your critical-to-quality attributes (CQAs) using both screening and response surface designs be able to design and analyze screening designs including a factorial, fractional factorial, and D-optimal design understand the need for adding center points to a design be able to design and analyze response surface designs including central composite designs (CCDs), Box-Behnken designs, and I-optimal designs be able to design and analyze definitive screening designs present results of DOE studies Outline: Introduction to Design of Experiments (DOE) steps to DOE defining critical-to-quality attributes (CQAs) identifying and prioritizing potential process parameters Screening Designs – Identifying Critical Process Parameters factorial designs fractional factorial designs D-optimal designs Response Surface Designs – Develop Functional Relationships and Establish Design Space Central Composite Designs (CCDs) Box-Behnken designs (Self Study) I-optimal designs Other Design of Experiments Classes and Training	View Outline and Register
Introduction to JMP (0.5 days)	December 5, 2024 1:00 pm	Introduction to JMP (0.5 days): The course provides an introduction on how to use JMP statistical software with an emphasis on importing data, summary tables, charts, and graphs. Introduction to JMP: import and arrange data for analysis including JMP data table productivity features perform data exploration create summaries, tables, charts, and graphs utilize Graph Builder Outline: 1. Introduction to JMP navigating JMP importing data tables in JMP column menu rows menu creating formulas 2. Graphical Analysis producing univariate graphs producing bivariate and multivariate graphs time series and maps	Sold Out
Quality by Design (QbD) using DOE (3.0 days)	December 10, 2024 9:00 am	Quality by Design (QbD) using DOE (3.0 days): This course focuses on This course focuses on how to establish a systematic approach to pharmaceutical development that is defined by Quality-by-Design (QbD) principles using design of experiments (DOE). In addition, this course teaches the application of statistics for setting specifications, assessing measurement systems (assays), developing a control plan as part of a risk management strategy, and ensuring process control/capability. All concepts are taught within the product quality system framework defined by requirements in regulatory guidance documents. Analyses in this course use the point-and-click interface of JMP Software by SAS. Quality by Design (QbD) using DOE Objectives: implement QbD principles from discovery through product discontinuation apply statistics to set specifications and validate measurement systems (assays) utilize risk management tools to identify and prioritize potential critical process parameters identify critical process parameters and develop a functional relationship between those process parameters and your critical-to-quality attributes (CQAs) establish your design space develop a control plan as part of a risk management strategy ensure your process is in (statistical) control and capable. Outline 1. Introduction to Quality by Design (QbD) • Quality by Design (QbD) principles • Product Quality System framework 2. Primer on Statistical Analysis • basic statistics • hypothesis testing • ANOVA and regression • Blocking 3. Foundational Requirements for QbD Studies • setting specifications • Measurement Systems Analysis (MSA) for assays 4. Introduction to Design of Experiments (DOE) • steps to DOE • defining critical-to-quality attributes (CQAs) • identifying and prioritizing potential process parameters 5. Screening Designs – Identifying Critical Process Parameters • factorial designs • fractional factorial designs • D-optimal designs 6. Response Surface Designs – Develop Functional Relationships and Establish Design Space • Central Composite Designs (CCDs) • Box-Behnken designs (Self Study) • I-optimal designs 7. Specialized Designs • Definitive Screening Designs • A Optimal Designs 8. Utilizing Systematic Understanding from QbD Studies • presenting results • developing a control plan as part of a risk management strategy • process control and capability 9. Advanced DOE Methodologies (Self Study) • split-plot designs • robust parameter design • supersaturated designs • constrained designs • mixture designs	Sold Out
Quality by Design for the Life Cycle of an Analytical Procedure (2.0 days)	December 11, 2024 9:00 am	Quality by Design for the Life Cycle of an Analytical Procedure (2.0 days): The pharmaceutical industry relies on analytical procedures for the combined practices of process and formulation development, process validation, and manufacturing, as well as both in-process and final-product testing. These test methods need not only be validated but reliable and fit for its intended use. This course provides a systematic approach to the lifecycle of analytical procedures that begins in procedure design and development, continues in procedure performance qualification, and is maintained in continued procedure performance verification. Quality by Design for the Life Cycle of an Analytical Procedure Apply a Quality by Design (QbD) approach throughout the lifecycle of an analytical procedure. Reference legal and regulatory documents reference requirements for analytical validation. Describe the principles of analytical validation referenced by those documents. Set analytical procedure validation requirements that are fit for their intended use. Apply the statistical tools for QbD throughout the lifecycle of an analytical procedure. Outline: Introduction to AQbD AQbD principles Primer on Statistical Analysis Basic Statistics Statistical intervals and hypothesis testing ANOVA and regression Process control charts and capability USP <1225> and ICH Q2 Stage 1: Procedure Design Defining fitness for intended use Design considerations Stage 2: Procedure Performance Qualification (PPQ) Analysis – FUV CD Analysis – AUC Stage 3: Continued Verification System suitability and analytical control. Continued verification.	Sold Out
Reliability Engineering and Survival Analysis (1.0 days)	December 19, 2024 9:00 am	Statistical Methods for Reliability Engineering and Survival Analyses: This course covers the graphical and quantitative methods used reliability and survival analysis using JMP software. We will explore statistical methods to characterize and predict reliability and probability of survival. Topics include censoring, probability distributions—particularly Weibull and Lognormal, sample size determination, degradation/stability analysis, reliability with covariates/predictor variables, survival analysis, and fitting parametric survival models. Statistical Methods for Reliability Engineering and Survival Analyses Objectives: Understand why and how to censor data from survival studies Be able to interpret nonparametric survival plots and models Determine the best probability distribution and parameter estimates to characterize reliability Be able to model the impact of predictor variables (covariates) Calculate sample size estimates for reliability demonstrations Model system performance with survival and parametric survival techniques 1. Introduction to Reliability Methods • Censoring and event plots • Functions and definitions: reliability, survival, hazard, cumulative • Nonparametric survival methods • Probability distributions for reliability analysis 2. Reliability and Survivability Models • Survival analysis • Reliability with a single predictor variable • Reliability with multiple predictor variables using parametric survival models 3. Advanced Approaches • Degradation and stability analysis • Sample size determination • Accelerated life tests	View Outline and Register
Spec Setting and Stability Analysis using JMP (0.5 days)	December 19, 2024 1:00 pm	Spec Setting and Stability Analysis using JMP (0.5 days): This course provides instruction on how to use JMP statistical software for setting specifications and stability analysis including degradation analysis using the Arrhenius acceleration factor. Spec Setting and Stability Analysis using JMP Objectives: know the recommended approaches for setting specifications be able to calculate the range, a reference interval, and tolerance interval understand how to adjust a reference interval to account for small sample sizes understand the purpose of a stability study know the need to evaluate stability dat understand the importance for a test of poolability of batches be able to use Analysis of Covariance (ANCOVA) is used to evaluate stability data be able to use JMP to determine expiries based upon the criteria outlined in the ICH guidance on evaluation of stability data understand the need for accelerated degradation analysis be able to fit a nonlinear function using the Arrhenius acceleration factor Outline: 1. Setting Specifications • Min, Max Intervals • Reference Intervals – Normal Distribution • Reference Intervals – Non-Normal Distribution (Self Study) • Tolerance Interval • Adjusted Reference Intervals for Small Sample Sizes 2. Stability Analysis • Purpose of stability analysis • Setting expiries – Situation 1 • Setting expiries – Situation 2 • Setting expiries – Situation 3 • Evaluating accelerated degradation analysis using the Arrhenius acceleration factor • Evaluating stability for non-linear degradation (self study)	Sold Out
Statistical Tools for Process Control and Capability Analysis (0.5 days)	December 18, 2024 1:00 pm	Statistical Tools for Process Control and Capability Analysis (0.5 days): This course provides instruction on how to apply statistical tools to ensure your process operates in a state of (statistical) control. Although multiple tools are demonstrated, the course focuses on specific tools associated with SPC and process capability. All concepts are taught within the quality system framework. Statistical Tools for Process Control and Capability Analysis Objectives: Be able to use tools to ensure your process is in (statistical) control and capable Generate and analyze a distribution and run chart of time series data Understand, generate, and analyze individual (I) and moving range (MR) control charts Understand, generate, and analyze mean (Xbar) and range (R) control charts Understand, generate, and analyze process capability analyses Outline: 1. Primer on Statistical Analysis for Process Control and Capability • Distribution • Time series charts 2. Statistical Process Control and Capability • Developing a control plan as part of a risk management strategy • Statistical process control • Process capability	View Outline and Register
Statistics for FDA Process Validation (3.0 days):	December 18, 2024 9:00 am	Statistics for FDA Process Validation (3.0 days): This course focuses on how to establish a systematic approach to implementing statistical methodologies into a process development and validation program consistent with the FDA guidance. This course teaches the application of statistics for setting specifications and assessing measurement systems (assays), using design of experiments (DOE) for process design, establishing a strategy for process qualification, and ensuring process control/capability. All concepts are taught within the three-stage product cycle framework defined by requirements in the process validation regulatory guidance documents. Statistics for FDA Process Validation Objectives: apply statistics to set specifications and validate measurement systems (assays) develop appropriate sample plans based upon confidence and power implement suitable statistical methods into a process validation program for each stage: Stage 1 – Process Design ◦ utilize risk management tools to identify and prioritize potential critical process parameters ◦ define critical process parameters and operating spaces for the commercial manufacturing process using design of experiments (DOE). Stage 2 – Process Qualification ◦ assess scale effects while incorporating large (pilot) scale data ◦ develop process performance qualification (PPQ) acceptance criteria by characterizing intra and inter-batch variability using process design data and batch homogeneity studies ◦ develop an appropriate sampling plan for PPQ. Stage 3 – Continued Process Verification ◦ develop a control plan as part of a risk management strategy ◦ collect and analyze product and process data ◦ ensure your process is in (statistical) control and capable. Outline: 1. Introduction to Statistics for Process Validation • Process Validation principles • Stages of Process Validation 2. Primer on Statistical Analysis • Basic statistics • Statistical intervals and hypothesis testing • ANOVA and regression • Time series charts 3. Foundational Requirements for Process Validation • Setting specifications • Analytical methodology • Sampling plans 4. Stage 1 – Process Design • Steps to DOE • Defining critical-to-quality attributes (CQAs) • Identifying and prioritizing potential process parameters • Screening designs • Response surface designs • Establishing a strategy for process control 5. Stage 2 – Process Qualification • Batch homogeneity • Setting PPQ acceptance criteria • Scale effects • Characterizing inter and intra-batch variability 6. Stage 3 – Continued Process Verification • Developing a control plan as part of a risk management strategy • Statistical process control • Process capability 7. Advanced Methodologies (Optional) • Advanced DOE • Advanced Process Control • Advanced Process Capability Appendix A – Capstone Exercise Appendix B – Reference Material	Sold Out
What's New in JMP 18....and JMP 17!	December 9, 2024 1:00 pm	During a recent What’s New in JMP 18 seminar, the customers kept asking about other new JMP features. These other new features were new, but they were new in JMP 17! JMP updates software so frequently, many customers don’t have time to learn new features of one version before the next version is out. This complementary seminar will benefit those customers that are still using JMP 17 as well as those that have updated to JMP 18. Thursday August 1, 2024 from 1:00 – 3:00 Eastern Time	Sold Out