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The Statistics and Machine Learning with R Workshop

You're reading from   The Statistics and Machine Learning with R Workshop Unlock the power of efficient data science modeling with this hands-on guide

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781803240305
Length 516 pages
Edition 1st Edition
Languages
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Author (1):
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Liu Peng Liu Peng
Author Profile Icon Liu Peng
Liu Peng
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Table of Contents (20) Chapters Close

Preface 1. Part 1:Statistics Essentials
2. Chapter 1: Getting Started with R FREE CHAPTER 3. Chapter 2: Data Processing with dplyr 4. Chapter 3: Intermediate Data Processing 5. Chapter 4: Data Visualization with ggplot2 6. Chapter 5: Exploratory Data Analysis 7. Chapter 6: Effective Reporting with R Markdown 8. Part 2:Fundamentals of Linear Algebra and Calculus in R
9. Chapter 7: Linear Algebra in R 10. Chapter 8: Intermediate Linear Algebra in R 11. Chapter 9: Calculus in R 12. Part 3:Fundamentals of Mathematical Statistics in R
13. Chapter 10: Probability Basics 14. Chapter 11: Statistical Estimation 15. Chapter 12: Linear Regression in R 16. Chapter 13: Logistic Regression in R 17. Chapter 14: Bayesian Statistics 18. Index 19. Other Books You May Enjoy

Introducing the matrix determinant

The determinant of a matrix is a special scalar value that can be calculated from a matrix. Here, the matrix needs to be square, meaning it has an equal number of rows and columns. For a 2x2 square matrix, the determinant is simply calculated as the difference between the product of the diagonal elements and the off-diagonal elements.

Mathematically, suppose our 2x2 matrix is A = [a b c d ]. Its determinant, |A|, is thus calculated as follows:

det(A) = |A| = ad bc

Please do not confuse these vertical lines with the absolute operation sign. They represent the determinant in the context of a matrix, and the determinant of a matrix can be negative as well.

Let’s say our 2x2 matrix is A = [2 6 1 8]. We can find its determinant like so:

|A| = 2 * 8 6 * 1 = 10

Calculating the determinant of a matrix is the easy part, but understanding its use is of equal importance. Before we...

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