Engineering Statistics and Data Analysis
 
3 Days

Course Description and Audience:
ESDA is for Engineers, Scientists and Managers who routinely analyze data for product
development, qualification and control.  Areas of focus are; analysis of data for basic
product development and manufacturing applications including foundation statistics,
distribution analysis, capability assessment, sensitivity prediction, comparison tests,
sample size selection and model fitting.

 

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

  1. Understand the ideas associated with sampling and data collection.
  2. Demonstrate the ability to evaluate distributions.
  3. Select appropriate sample sizes for performance evaluation.
  4. Conduct comparative tests using data.                            
  5. Use regression techniques in order to analyze the results and make
    process / product improvements.
  6. Select an appropriate analysis technique based on the type of data.

Software:  JMP  .      
Prerequisites:  None.

Detailed Course Outline:
  
Introduction to (JMP  )                                                             
Table commands                                                           
Column commands                                                        
Row commands                                                            
Subset, Stack and Join commands                                             
Saving data and graphs 
                                                
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
Data and tolerance intervals  (normal)                                          
Process capability (normal) and non-normal distribution fitting     
                         
Nominal X, Continuous Y  
Contour plots, Components of Variance and REML                                   
Sample size for the mean and standard deviation                                    
t test – one sample, two sample and paired                                                                   
Test for differences in variances                                                                     
One-way ANOVA and N way ANOVA           
                                                     
Continuous X, Continuous Y
Simple linear regression, correlation                                                     
Multiple Regression and 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
Logistic regression