Adsurgo provides training, consulting, and analytical workflow automation.

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

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

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

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