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Industrial Statistics: A Computer Based Approach with Python
Publisher: Springer International Publishing;
1st edition (August 5, 2023) Errata: See known errata here |
Industrial Statistics: A Computer Based Approach with Python is a companion volume to the book Modern Statistics: A Computer Based Approach with Python.
This part of the repository contains:
notebooks: Python code of individual chapters in Jupyter notebooks - download notebooks and data as notebooks.zipcode: Python code for solutions as plain Python files - download all as code.zipsolutions manual: Solutions_IndustrialStatistics.pdf: solutions of exercisessolutions: Python code for solutions in Jupyter notebooks - download all as solutions.zipall: zip file with all files combined - download all as all.zipdatafiles: zip file with all data files - download all as data_files.zip - themistatpackage gives you already access to all datafiles, you only need to download this file if you want to use it with different software
All the Python applications referred to in this book are contained in a package called mistat available
for installation from the Python package index https://pypi.org/project/mistat/.
The mistat packages is maintained in a GitHub repository at https://github.com/gedeck/mistat.
You can explore the code on
Instructions on installing Python and required packages are here.
These Python packages are used in the code of Industrial Statistics:
- mistat (for access to data sets and additional functionality)
- matplotlib
- numpy
- pandas
- scipy
- statsmodels
- seaborn
- pingouin
- lifelines
- dtreeviz
- svglib
- pwlf
- pyDOE3
- diversipy
- pylibkriging
- inspyred
- pymc
- arviz
- aesara
The notebook InstallPackages.ipynb contains the pip command to install the required packages. Note that some of the packages may need to be pinned to specific versions.
If you have a problem with visualizing the decision tree or creating a network graph, follow the installation instructions for graphviz in the dtreeviz github site. On Windows, the problem is usually resolved by adding the path to the graphviz binaries to the PATH system variable.
Chapter 1: Introduction to Industrial Statistics (sample 1)
Chapter 2: Basic Tools and Principles of Process Control (sample 2)
Chapter 3: Advanced Methods of Statistical Process Control (sample 3)
Chapter 4: Multivariate Statistical Process Control (sample 4)
Chapter 5: Classical Design and Analysis of Experiments (sample 5)
Chapter 6: Quality by Design (sample 6)
Chapter 7: Computer Experiments (sample 7)
Chapter 8: Cybermanufacturing and Digital Twins (sample 8)
Chapter 9: Reliability Analysis (sample 9)
Chapter 10: Bayesian Reliability Estimation and Prediction (sample 10)
Chapter 11: Sampling Plans for Batch and Sequential Inspection (sample 11)