Copying 'Introduction to Multivariate Analysis' in Python - Prologue: I've Started Copying in Python

Book authors: Dr. Sadao Ishimura, Dr. Koshiro Ishimura

This article is an introduction to the book 'Introduction to Multivariate Analysis' and the motivation behind my Python copying activities.

Introduction to the book 'Introduction to Multivariate Analysis'

This series is a Python copy of the book 'Introduction to Multivariate Analysis' (Tokyo Tosho, referred to as the 'text').

The text was first published in February 2007 and is a bestselling introductory book on multivariate analysis. I discovered this text through a recommendation from kenken, whom I met in the data analysis community on X (SNS).

I had a wonderful learning experience with the sister book 'Introduction to Statistical Analysis,' which I practiced beforehand, and I introduced it on my blog.

Without hesitation, I continued my learning of multivariate analysis using Dr. Ishimura's series of books.

Multivariate analysis is a tool for analyzing 'data with multiple columns (variables),' like an Excel spreadsheet.

You select the tool to apply based on the purpose of the analysis and the content of the data.
You can learn the following major multivariate analysis tools in the text.

• Multiple Regression Analysis: An analysis tool where the objective variable is a 'quantitative variable'

• Discriminant Analysis: An analysis tool where the objective variable is a 'qualitative variable'

• Principal Component Analysis: A tool for finding 'comprehensive indicators' from multiple variables

• Factor Analysis: A tool for finding 'common points' behind multiple variables

• Cluster Analysis: A tool for grouping similar items from within data

Furthermore, you can touch upon Quantification Theory.

• Quantification Method I: Equivalent to multiple regression analysis when explanatory variables are qualitative data

• Quantification Method II: Equivalent to discriminant analysis when explanatory variables are qualitative data

• Quantification Method III: Equivalent to principal component analysis when variables are qualitative data

On to Python copying

The textbook uses a style of learning multivariate analysis with 'paper and pen'.
While it occasionally includes analysis results output by the statistical software SPSS, there are times when the solution process becomes a black box, making it difficult to calculate the results.

When it comes to practicing multivariate analysis, I found myself thinking, if only I could calculate this quickly with the help of a computer......
As usual, I practiced Python transcription!
There are many ways to learn from a textbook, and all are welcome!

The most difficult part was calculating the same values as the SPSS analysis results in the textbook.
There were cases where the analysis logic of SPSS and the Python library differed, and I couldn't find the cause of the discrepancy, leading to different calculation results than SPSS.
Please accept this as the true pleasure of 'detour transcription,' where you wander off to various places.

Towards Generative AI-Enhanced Learning

And then...
I also practiced 'Using ChatGPT as a Learning Assistant', which I got hooked on while writing the blog for the sister book!
The version used is GPT-o4-mini-high.
It feels cooler to use compared to GPT-4o.

ChatGPT played a major role in the following scenes.

• Drafting opening and closing remarks

• Drafting simple explanations of multivariate analysis methods

• Deep diving into multivariate analysis methods and understanding calculation logic

• Researching Python libraries and prototyping sample code

I hope you can empathize with the fact that you can do things like this with generative AI 🍀

📣 Additional Information 📣
Upgraded to ChatGPT 5 on August 8, 2025.

The blog table of contents is here

Please take a look at the article titles in the table of contents and think about the articles!

Citation Notation

This article cites text and data published in the book listed in the source, and has been written with appropriate modifications to the published text and data.
[Source]
'Introduction to Multivariate Analysis', 13th printing, Authors: Sadao Ishimura, Koshiro Ishimura, Tokyo Tosho

The illustrations in the article are borrowed from 'Cute Free Material Collection Irasutoya'.
Thank you!

Series articles

Next article

Table of Contents

Blog Introduction

I am writing seven series of articles on note.
Please feel free to take a look!

1. Relaxed Statistics

This is a blog that roughly explores probability and statistics using the Statistics Grade 2 exam workbook as a guide.
Feel free to read it as if it were casual conversation. Please take a look.
It corresponds to the Statistics Grade 2 Official Workbook CBT version.
Sample code for Python and EXCEL is also available.

2. Experiment! Fun Bayesian Modeling 1 & 2 with PyMC Ver.5

I will draw and analyze the Bayesian models used in psychological research from the books 'Fun Bayesian Modeling' and 'Fun Bayesian Modeling 2' using PyMC Ver.5.
Like this book, many Bayesian models are written in R and Stan.
I will strive to explore the possibilities of PyMC and make Bayesian modeling easy to practice.
Since these are familiar and easy-to-visualize themes, please try running them with PyMC and let's enjoy it together!

3. Experiment! Bayesian Modeling from Iwanami Data Science 1 with PyMC Ver.5

I will draw and analyze the Bayesian models by four Bayesians from the book 'Experiment! Iwanami Data Science Vol. 1' using PyMC Ver.5.
This book is a great resource for learning the basics of Bayesian programming.
I feel like I've become friends with Bayesian methods by happily running PyMC models.
Everyone, please try running them with PyMC and let's play and learn together!

4. Fun Copying: Bayesian, Python, etc.

I will blog about the results of my 'book copying activities' for Bayesian, Python, and others.
I am mainly working on translations into Python.
I hope this serves as sample code for fellow learners who are also copying code 🍀

5. Introduction to Time Series Analysis for Psychology with R and Stan, using Python and PyMC Ver.5

I will practice the time series analysis from the book 'Introduction to Time Series Analysis for Psychology with R and Stan' using Python and PyMC Ver.5.
This book is packed with time series analysis themes!
I realized the depth of time series analysis.
I will enjoy learning time series analysis with my favorite language, Python.

6. Writing about Data Science-like things

I write columns on statistics, data analysis, AI, machine learning, and Python on an irregular basis.
There are many articles related to statistics and data science books.
Series on 'Statistics', 'Python', 'Math and Python', and 'R' have been created.

7. Python Machine Learning Programming Practice Log

I wrote articles about my various thoughts when studying the book 'Python Machine Learning Programming: PyTorch & scikit-learn Edition'.
This book is a textbook for scikit-learn and PyTorch.
Please feel free to try it out if you like.

Thank you very much for reading until the end.