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Hands-On Data Science with Anaconda

You're reading from   Hands-On Data Science with Anaconda Utilize the right mix of tools to create high-performance data science applications

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788831192
Length 364 pages
Edition 1st Edition
Languages
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Authors (2):
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James Yan James Yan
Author Profile Icon James Yan
James Yan
Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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Table of Contents (15) Chapters Close

Preface 1. Ecosystem of Anaconda 2. Anaconda Installation FREE CHAPTER 3. Data Basics 4. Data Visualization 5. Statistical Modeling in Anaconda 6. Managing Packages 7. Optimization in Anaconda 8. Unsupervised Learning in Anaconda 9. Supervised Learning in Anaconda 10. Predictive Data Analytics – Modeling and Validation 11. Anaconda Cloud 12. Distributed Computing, Parallel Computing, and HPCC 13. References 14. Other Books You May Enjoy

Several multivariate linear models

As we mentioned at the beginning of the chapter, we could show several applications of multivariable linear models. The first one is a three-factor linear model. The general formula is quite similar to the one-factor linear model, shown here:

The definitions are the same as before. The only difference is that we have three independent variables instead of one. Our objective is to estimate four parameters, one intercept plus three coefficients:

For example, the equation of the famous Fama-French 3-factor model is given, where Ri is the stock i's return and Rm is the market return. SMB (Small Minus Big) is defined as the returns of the small portfolios minus the returns of the big portfolios and HML (High Minus Low) is the difference of returns of high book-to-market portfolios minus the returns of low book-to-market portfolios. (See the...

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