Engineering Statistics and Data Analysis

Course Description:
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.

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.

  

Course Objectives:
Upon completion of the course the participants will be able to:

  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 improvements.
  7. Select appropriate analysis technique based on type of data.
  8. Apply JMP to data analysis problems.

Prerequisites:  There are no prerequisites for this course.

Detailed Course Outline:

Introduction to JMP                                                  
Table commands
Column commands
Row commands
Subset commands
Saving Scripts, Journals and Projects
                                                
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
                         
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)  
                                                     
Continuous X, Continuous Y
Simple linear regression, correlation
Multiple regression
ANCOVA       
                                                           
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                 
                                              
Continuous X, Nominal Y and Partition
Logistic regression
Nominal logistic regression (optional)
Recursive partitioning

Nonlinear Modeling
Nonlinear modeling