Engineering Statistics & Data Analysis
Statistical and Analytical Courses
ESDA is specifically designed to meet the analytical needs of those individuals working within a variety of industries. Areas of focus include: JMP basics, analysis of data for basic engineering and scientific applications including statistics, distribution analysis, capability assessment, variation analysis, comparison tests, sample size selection, hypothesis testing, confidence intervals and multiple factor modeling. Presentation of the course material is designed for 24 hours of instruction.

Download Curriculum

Download the full curriculum for Statistical and Analytical courses in PDF format.

Biotech, Pharmaceutical & Medical Device Courses

Systematic product development, Quality by Design courses, consulting services and analytical training for biotechnology, pharmaceutical and medical device industries. QbD provides guidance to facilitate design of products and processes that maximize the product’s efficacy and safety profile while enhancing product manufacturability and control.

Lean Six Sigma

Complete curriculum for new product development, manufacturing and business process performance optimization.

Tools & Templates

Development tools and templates created by Thomas A. Little Consulting have been used by numerous companies to aid and support various aspects of product development, problem solving, data analysis and risk assessment.

Attendees
ESDA is required for all scientists, engineers and quality professionals who actively work on all aspects of discovery, product and process development where the goal is to characterize, optimize and improve product and process performance.
Prerequisites
There are no prerequisites for this course.
Course Objectives
  1. Use data to solve engineering and scientific problems.
  2. Understand the ideas associated with sampling and data collection.
  3. Demonstrate the ability to evaluate distributions.
  4. Select appropriate sample sizes for performance evaluation.
  5. Conduct comparative tests using data.
  6. Use regression techniques in order to analyze data and make process/product improvement.
  7. Select appropriate analysis technique based on type of data.
  8. Apply JMP to data analysis problems.
Detailed Course Outline
Section I: Introduction to JMP
Table commands
Column commands
Row commands
Subset commands
Saving Scripts, Journals and Projects
Section II: Statistics Foundations & Distribution Analysis
Measures of center and spread
Standard error and central limit theorem
Normal distribution
t distribution and confidence intervals
Test for normality
Individuals and tolerance intervals (normal)
Process capability (normal)
Nonnormal distribution fitting and process capability
Section III: Nominal X, Continuous Y
Contour plots, Components of Variance, REML and POV
Sample size for the mean and standard deviation
t test – one sample
t test – two sample
Test for differences in variances
t test – paired
One-way ANOVA and F test
N-way ANOVA
Nonparametric data analysis (optional)
Section IV: Continuous X, Continuous Y
Simple linear regression, correlation
Multiple regression
ANCOVA
Section V: Nominal X, Nominal Y
Mean and sigma for proportion defective
Sample size and statistical tests for proportion defective
Mean and sigma for defect per unit
Chi-square test for defects and proportion defective
Pareto graphs and cross tabs analysis
Section VI: Continuous X, Nominal Y and Partition
Logistic regression
Nominal logistic regression (optional)
Recursive partitioning
Section VII: Nonlinear Modeling
Nonlinear modeling
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