Expertise & Capabilities

Adsurgo is a professional services company solving problems through direct engagement consulting services and training workshops focused on the use of analytics.

CAPABILITIES

What We Do

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Analytical Workflow Automation

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Industries

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Event	Date	Description

Applied Statistics for Scientists (2.0 days)	November 10, 2026 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	View Outline and Register
Design of Experiments (DOE) using JMP (1.0 days)	November 11, 2026 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

Event

Date

Description

Applied Statistics for Scientists (2.0 days)

November 10, 2026 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

View Outline and Register

Design of Experiments (DOE) using JMP (1.0 days)

November 11, 2026 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

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Adsurgo LLC

1141 N Loop E Suite 105 Unit 463
San Antonio, TX 78232

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Expertise & Capabilities

What We Do

Applied Statistics

Analytical Workflow Automation

Design of Experiments

Reliability and Survivability

Data Visualization

Quality Control

Industries

Biopharma & Healthcare

DoD/Government

Manufacturing

Technology

Energy

Financial Services