Data Analysis with Cornerstone

Cornerstone makes data collection, data aggregation, and data analysis simple, compact, and automatically replicable. In this course, you will learn how to use Cornerstone software to quickly “get familiar” with your data, identify patterns, and evaluate them according to a consistent concept. 

Course outline: 

1. Introduction to Cornerstone 
2. Data acquisition 
3. Data preprocessing 
4. Graphical methods and their connection to statistics 
5. Basic statistical concepts (Summary statistics, confidence intervals, distribution fitting) 
6. Regression, Principal Component Analysis and MANOVA 
7. Practical engineering examples 
   

Overview

Course Content

Introduction into Cornerstone:

• Data reading
• Data source definition
• Using read, reread, drag & drop
• Joining and merging
  
  

Data preprocessing

• Column attributes (data type usage and format)
• Add manual and computed columns
• Create subsets
  
  

Data visualizing

• Graph types
• Customizing graph attributes
• Dynamic graphic tools (Cornerstone brush, labeling and transparency tool, copy & paste)
• Connection of graphs with statistical methods
• Exporting graphs
  
  

Basic statistics

• Distributions, confidence intervals
• Summary statistics and data aggregation
  
  

Regression (incl. analysis of variance)

• Model approach
• Checking assumption (goodness of fit, outliers, transformations)
• Graphs (adjusted response, predicted responses)
• Optimization

Principal component analysis and MANOVA (optional)

• Using principal components for correlational analysis
• Principal component as a data compression tool for regression
• Using MANOVA as a decision tool for assigning categorical outcome (discrimination analysis)

Basic quality engineering (optional)

• Measurement System Analysis
• Statistical Process Control
• Process capability (Cpk analysis)

Practical engineering examples

Objective

Target Groups

Background Knowledge

• Understand the workmap concept of analysis workflow in Cornerstone. 
• Reuse of analysis with upcoming data (no Macro recorder needed).
• Structure data (build subsets, add computed columns, join data). 
• Make use of standard and innovative graph types. 
• Combine graphs, data, and analysis with interactive graph tools. 
• Understand basic statistical concepts as they apply to engineering problems. 
• Use compact functions to build models and gain insights.
• Regression, Principal Component Analysis (MANOVA).
  
  

Engineers, technicians, analysts, and scientists who will process and analyze data .

Background Knowledge: 

• Mathematical thinking  
• Interest in finding the hidden value in your data  
• Basic knowledge of statistics 

Learning tools: 

Personalized training material to accompany the training.