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Linear Regression And Correlation: A Beginner's Guide


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English | June 8th, 2017 | 213 Pages | AZW3 | 4.60 MB

If you are looking for a short beginners guide packed with visual examples, this book is for you.

Linear Regression is a way of simplifying a group of data into a single equation. For instance, we all know Moore's law: that the number of transistors on a computer chip doubles every two years. This law was derived by using regression analysis to simplify the progress of dozens of computer manufacturers over the course of decades into a single equation. Correlation is a way of calculating how much two sets of numbers change together. In addition to being part of the regression analysis, correlation is heavily used in investment industries, for instance, to determine if two stocks are likely to change value together or independently.

This book goes through how to calculate correlation and linear regression and works through multiple examples of how to do it. Just as importantly, this book is loaded with visual examples of what correlation is and how to use linear regression. This book doesn't assume that you have prior in-depth knowledge of statistics or that you regularly use an advanced statistics software package. If you know what an average is and can use Excel, this book will build the rest of the knowledge, and do so in an intuitive way.
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