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Data Analysis with Cornerstone

Empower your team with engineering data analytics

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

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

Objectives

  • 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)

Target groups

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

Duration

  • On site 2 days, 8 hours per day
  • Online 4 days, 4 hours per day

Learning tools

  • Personalized training material to accompany the training

Outline

  • Data acquisition
  • Data preprocessing
  • Graphical methods and their connection to statistics
  • Basic statistical concepts (Summary statistics, confidence intervals, distribution fitting)
  • Regression, Principal Component Analysis and MANOVA
  • Practical engineering examples

Training fee

  • € 1,490.00

The speakers

Registration

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