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The Statistics and Calculus with Python Workshop

You're reading from  The Statistics and Calculus with Python Workshop

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781800209763
Pages 740 pages
Edition 1st Edition
Languages
Concepts
Authors (6):
Peter Farrell Peter Farrell
Profile icon Peter Farrell
Alvaro Fuentes Alvaro Fuentes
Profile icon Alvaro Fuentes
Ajinkya Sudhir Kolhe Ajinkya Sudhir Kolhe
Profile icon Ajinkya Sudhir Kolhe
Quan Nguyen Quan Nguyen
Profile icon Quan Nguyen
Alexander Joseph Sarver Alexander Joseph Sarver
Profile icon Alexander Joseph Sarver
Marios Tsatsos Marios Tsatsos
Profile icon Marios Tsatsos
View More author details

Table of Contents (14) Chapters

Preface
1. Fundamentals of Python 2. Python's Main Tools for Statistics 3. Python's Statistical Toolbox 4. Functions and Algebra with Python 5. More Mathematics with Python 6. Matrices and Markov Chains with Python 7. Doing Basic Statistics with Python 8. Foundational Probability Concepts and Their Applications 9. Intermediate Statistics with Python 10. Foundational Calculus with Python 11. More Calculus with Python 12. Intermediate Calculus with Python Appendix

Inferential Statistics

Unlike descriptive statistics, where our goal is to describe various characteristics of a dataset using specific quantities, with inferential statistics, we'd like to perform a particular statistical modeling process on our dataset so that we can infer further information, either about the dataset itself or even about unseen data points that are from the same population.

In this section, we will go through a number of different methods of inferential statistics. From these discussions, we will see that each method is designed for specific data and situations, and it is the responsibility of the statistician or machine learning engineer to appropriately apply them.

The first method that we will discuss is one of the most fundamental in classical statistics: t-tests.

T-Tests

In general, t-tests (also known as Student's t-tests) are used to compare two mean (average) statistics and conclude whether they are different enough from each other...

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