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Multivariate Analysis:

The SIMPLEST Guide In The Universe

​Only $20 !!!

When searching for relationships in your data (associations and correlations), most results you’ll get are wrong. In this book you’re going to learn precisely why, and learn how to make sure you get the correct results first time, every time.

In this book you'll learn the answers to questions such as

  • Why should I do multivariate analysis?
  • How do I choose which type of multivariate test to use?
  • How do I interpret the results of multivariate tests?
  • What should I do when my univariate and multivariate results do not agree?

FIND THE TRUE STORY OF YOUR DATA

In Multivariate Analysis – The Simplest Guide in the Universe you’ll learn a holistic method of discovering the story of all the relationships in your data. 

This holistic method is about selecting the correct multivariate tests following your univariate analysis and using all the results in a single strategic framework to give you confidence that the story you discover is likely to be the true story of your data.

Why should you care what I say?

I have worked with data for most of my adult life. I have worked with data from the Visible Human Project, the Human Genome Project, a major European Soil Database, Medical Imaging Data, and loads and loads of Data from Clinical Studies Research.

I now help other scientists and business analysts just like you get the story of their data quickly and easily.

Lee Baker - CEO Chi-Squared Innovations
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“​My Mission Is To Unleash Your Inner Data Ninja”


​Lee Baker

Multivariate Analysis: The simplest Guide in the Universe

Multivariate Analysis:

The Simplest Guide In The Universe

Multivariate Analysis – The Simplest Guide in the Universe is written in plain English with a focus on understanding the data, how to work with it, choose the right ways to analyse it, select the correct statistical tools and how to interpret the results in a way that is easy to understand.

It enables researchers of all backgrounds to understand and ​critically evaluate ​the results of analyses that they are likely to encounter in their own research and in that of others.

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